{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mfd_date</th>\n",
       "      <th>Interest_O_N</th>\n",
       "      <th>Interest_1_W</th>\n",
       "      <th>Interest_2_W</th>\n",
       "      <th>Interest_1_M</th>\n",
       "      <th>Interest_3_M</th>\n",
       "      <th>Interest_6_M</th>\n",
       "      <th>Interest_9_M</th>\n",
       "      <th>Interest_1_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20130701</td>\n",
       "      <td>4.456</td>\n",
       "      <td>5.423</td>\n",
       "      <td>6.0400</td>\n",
       "      <td>6.8800</td>\n",
       "      <td>5.2950</td>\n",
       "      <td>4.2390</td>\n",
       "      <td>4.2820</td>\n",
       "      <td>4.4125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20130702</td>\n",
       "      <td>3.786</td>\n",
       "      <td>4.750</td>\n",
       "      <td>5.0740</td>\n",
       "      <td>5.8000</td>\n",
       "      <td>5.2110</td>\n",
       "      <td>4.2344</td>\n",
       "      <td>4.2808</td>\n",
       "      <td>4.4070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20130703</td>\n",
       "      <td>3.400</td>\n",
       "      <td>4.242</td>\n",
       "      <td>4.6580</td>\n",
       "      <td>5.2000</td>\n",
       "      <td>5.1480</td>\n",
       "      <td>4.2300</td>\n",
       "      <td>4.2796</td>\n",
       "      <td>4.4022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20130704</td>\n",
       "      <td>3.348</td>\n",
       "      <td>3.938</td>\n",
       "      <td>4.4640</td>\n",
       "      <td>5.1020</td>\n",
       "      <td>5.0290</td>\n",
       "      <td>4.2287</td>\n",
       "      <td>4.2776</td>\n",
       "      <td>4.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20130705</td>\n",
       "      <td>3.380</td>\n",
       "      <td>3.816</td>\n",
       "      <td>4.2950</td>\n",
       "      <td>4.7885</td>\n",
       "      <td>4.9390</td>\n",
       "      <td>4.2273</td>\n",
       "      <td>4.2749</td>\n",
       "      <td>4.4000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>20140825</td>\n",
       "      <td>2.841</td>\n",
       "      <td>3.363</td>\n",
       "      <td>3.8860</td>\n",
       "      <td>4.1990</td>\n",
       "      <td>4.6758</td>\n",
       "      <td>4.8800</td>\n",
       "      <td>4.9394</td>\n",
       "      <td>5.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>20140826</td>\n",
       "      <td>2.823</td>\n",
       "      <td>3.318</td>\n",
       "      <td>3.8760</td>\n",
       "      <td>4.2000</td>\n",
       "      <td>4.6695</td>\n",
       "      <td>4.8730</td>\n",
       "      <td>4.9363</td>\n",
       "      <td>5.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>20140827</td>\n",
       "      <td>2.861</td>\n",
       "      <td>3.349</td>\n",
       "      <td>3.9828</td>\n",
       "      <td>4.1780</td>\n",
       "      <td>4.6680</td>\n",
       "      <td>4.8729</td>\n",
       "      <td>4.9365</td>\n",
       "      <td>5.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>20140828</td>\n",
       "      <td>2.876</td>\n",
       "      <td>3.538</td>\n",
       "      <td>4.0500</td>\n",
       "      <td>4.1330</td>\n",
       "      <td>4.6689</td>\n",
       "      <td>4.8727</td>\n",
       "      <td>4.9373</td>\n",
       "      <td>5.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>20140829</td>\n",
       "      <td>2.908</td>\n",
       "      <td>3.672</td>\n",
       "      <td>4.1990</td>\n",
       "      <td>4.1140</td>\n",
       "      <td>4.6659</td>\n",
       "      <td>4.8726</td>\n",
       "      <td>4.9364</td>\n",
       "      <td>5.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mfd_date  Interest_O_N  Interest_1_W  Interest_2_W  Interest_1_M  \\\n",
       "0    20130701         4.456         5.423        6.0400        6.8800   \n",
       "1    20130702         3.786         4.750        5.0740        5.8000   \n",
       "2    20130703         3.400         4.242        4.6580        5.2000   \n",
       "3    20130704         3.348         3.938        4.4640        5.1020   \n",
       "4    20130705         3.380         3.816        4.2950        4.7885   \n",
       "..        ...           ...           ...           ...           ...   \n",
       "289  20140825         2.841         3.363        3.8860        4.1990   \n",
       "290  20140826         2.823         3.318        3.8760        4.2000   \n",
       "291  20140827         2.861         3.349        3.9828        4.1780   \n",
       "292  20140828         2.876         3.538        4.0500        4.1330   \n",
       "293  20140829         2.908         3.672        4.1990        4.1140   \n",
       "\n",
       "     Interest_3_M  Interest_6_M  Interest_9_M  Interest_1_Y  \n",
       "0          5.2950        4.2390        4.2820        4.4125  \n",
       "1          5.2110        4.2344        4.2808        4.4070  \n",
       "2          5.1480        4.2300        4.2796        4.4022  \n",
       "3          5.0290        4.2287        4.2776        4.4000  \n",
       "4          4.9390        4.2273        4.2749        4.4000  \n",
       "..            ...           ...           ...           ...  \n",
       "289        4.6758        4.8800        4.9394        5.0000  \n",
       "290        4.6695        4.8730        4.9363        5.0000  \n",
       "291        4.6680        4.8729        4.9365        5.0000  \n",
       "292        4.6689        4.8727        4.9373        5.0000  \n",
       "293        4.6659        4.8726        4.9364        5.0000  \n",
       "\n",
       "[294 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mfd_bank=pd.read_csv('./Purchase Redemption Data/mfd_bank_shibor.csv')\n",
    "mfd_bao=pd.read_csv('./Purchase Redemption Data/mfd_day_share_interest.csv')\n",
    "\n",
    "mfd_bank\n",
    "# len(mfd_bank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mfd_date</th>\n",
       "      <th>Interest_1_W</th>\n",
       "      <th>mfd_7daily_yield</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20130701</td>\n",
       "      <td>5.423</td>\n",
       "      <td>6.307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20130702</td>\n",
       "      <td>4.750</td>\n",
       "      <td>6.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20130703</td>\n",
       "      <td>4.242</td>\n",
       "      <td>6.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20130704</td>\n",
       "      <td>3.938</td>\n",
       "      <td>5.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20130705</td>\n",
       "      <td>3.816</td>\n",
       "      <td>5.739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>20140825</td>\n",
       "      <td>3.363</td>\n",
       "      <td>4.139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>20140826</td>\n",
       "      <td>3.318</td>\n",
       "      <td>4.131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>20140827</td>\n",
       "      <td>3.349</td>\n",
       "      <td>4.123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>20140828</td>\n",
       "      <td>3.538</td>\n",
       "      <td>4.116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>20140829</td>\n",
       "      <td>3.672</td>\n",
       "      <td>4.123</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mfd_date  Interest_1_W  mfd_7daily_yield\n",
       "0    20130701         5.423             6.307\n",
       "1    20130702         4.750             6.174\n",
       "2    20130703         4.242             6.034\n",
       "3    20130704         3.938             5.903\n",
       "4    20130705         3.816             5.739\n",
       "..        ...           ...               ...\n",
       "289  20140825         3.363             4.139\n",
       "290  20140826         3.318             4.131\n",
       "291  20140827         3.349             4.123\n",
       "292  20140828         3.538             4.116\n",
       "293  20140829         3.672             4.123\n",
       "\n",
       "[294 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mfd_bao\n",
    "\n",
    "#按照日期合并两个表\n",
    "mfd=pd.merge(mfd_bank,mfd_bao,on='mfd_date',how='left')\n",
    "mfd=pd.DataFrame(data=mfd,columns=['mfd_date','Interest_1_W','mfd_7daily_yield'])\n",
    "mfd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2013ae70c90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhcAAAGdCAYAAAChGlFrAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/H5lhTAAAACXBIWXMAAA9hAAAPYQGoP6dpAACZOElEQVR4nO2dd3hb5dmHb21vO3YcJ07i7EEm2QkhJIFACLvsVfZsaKFp+QodlFFGJy2ztFCgzLKhjEIISSCQkEESsndiZzpx4j0lne+PV+dYsiVbkiVbkp/7unxJ1jqvZemc33nG7zFpmqYhCIIgCIIQIcwdvQBBEARBEBILEReCIAiCIEQUEReCIAiCIEQUEReCIAiCIEQUEReCIAiCIEQUEReCIAiCIEQUEReCIAiCIEQUEReCIAiCIEQUa3tv0O12s3//ftLT0zGZTO29eUEQBEEQwkDTNCoqKsjPz8dsbjk20e7iYv/+/fTu3bu9NysIgiAIQgQoKiqiV69eLT6m3cVFeno6oBaXkZHR3psXBEEQBCEMysvL6d27t3Ecb4l2Fxd6KiQjI0PEhSAIgiDEGcGUNEhBpyAIgiAIESVkcVFRUcEdd9xBnz59SE5O5oQTTmDFihXRWJsgCIIgCHFIyOLihhtuYP78+bz00kusW7eO0047jVmzZrFv375orE8QBEEQhDjDpGmaFuyDa2pqSE9P5/333+fMM880bh83bhxz5szhd7/7XauvUV5eTmZmJmVlZVJzIQiCkGC4XC4aGho6ehlCGFgsFqxWa8CailCO3yEVdDqdTlwuF0lJST63Jycns2TJklBeShAEQUgwKisr2bt3LyGcswoxRkpKCj169MBut7fpdUISF+np6UyZMoUHHniA4447jry8PF577TWWLl3KwIED/T6nrq6Ouro64/fy8vI2LVgQBEGIPVwuF3v37iUlJYXc3FwxSYwzNE2jvr6ew4cPs2vXLgYNGtSqUVZLhNyK+tJLL3HdddfRs2dPLBYLY8eO5bLLLmPVqlV+H//www9z3333hb1AQRAEIfZpaGhA0zRyc3NJTk7u6OUIYZCcnIzNZmPPnj3U19c3y1KEQsiyZMCAASxevJjKykqKiopYvnw5DQ0N9O/f3+/j7777bsrKyoyfoqKisBcrCIIgxDYSsYhv2hKt8CZsE63U1FRSU1M5duwYn376KX/4wx/8Ps7hcOBwOMJeoCAIgiAI8UXI4uLTTz9F0zSGDBnC9u3bufPOOxk6dCjXXnttNNYnCIIgCEKcEXL8o6ysjLlz5zJ06FCuuuoqTjzxRD799FNsNls01icIgiAIUWPGjBnccccdUd3Gvffey/HHHx/VbcQaIYuLiy++mB07dlBXV8eBAwd44oknyMzMjMbaBEEQBKFTU1tbyzXXXMPIkSOxWq2cd955Pvdv3rwZk8nEsmXLfG6fPHkySUlJ1NbW+rxWUlISzz33XNTXLbNFBCGO2HWkin98uYPqemdHL0UQhHbA5XKRnJzMT37yE2bNmtXs/qFDh9K9e3cWLVpk3FZRUcF3331Hbm6uj+hYunQpdXV1nHzyyVFft4gLQYgjHp2/lYc+3sz8jYc6eimC0CKaplFd7+yQn1BNvJxOJ7fddhuZmZl07dqV3/zmN8ZrvPTSS4wfP5709HS6d+/O5ZdfTnFxsfHcRYsWYTKZWLBgAePHjyclJYUTTjiBLVu2BNzejh076N+/P7fddlura01NTeXpp5/mxhtvpHv37n4fM3PmTB9xsWTJEgYPHszZZ5/tc/uiRYvo06cP/fr1C+JdaRvtPnJdEITwKa1RtsoVtRK5EGKbmgYXw+75tEO2vfH+2aTYgz+8vfjii1x//fUsX76clStXctNNN1FQUMCNN95IQ0MDDzzwAEOGDKG4uJh58+ZxzTXX8PHHH/u8xq9+9Sv+/Oc/k5ubyy233MJ1113H119/3Wxb33//PbNnz+b6668PamRGMMycOZOf/vSnOJ1OrFYrCxcuZMaMGUybNo2nnnqKe++9F4CFCxcyc+bMiGyzNURcCEIc0eB0A+AWe2VBiBi9e/fm0UcfxWQyMWTIENatW8ejjz7KjTfeyHXXXWc8rn///jz22GNMmDCByspK0tLSjPsefPBBpk+fDsBdd93FmWeeadQ46HzzzTecddZZ/OpXv+JnP/tZxNY/c+ZMqqqqWLFiBVOmTGHRokXceeednHjiiVx99dXU1taiaRrLly/nhhtuiNh2W0LEhSDEEfUuJS5cbhEXQmyTbLOw8f7ZHbbtUJg8ebKP+deUKVP485//jMvlYs2aNdx7772sXbuWY8eO4Xar72BhYSHDhg0znjNq1Cjjeo8ePQAoLi6moKDAePypp57Kgw8+GPHulIEDB9KrVy8WLVrE8OHDWb16NdOnT6dbt24UFBSwdOlSNE2jrq5OIheCIDSnQcSFECeYTKaQUhOxSG1tLbNnz2b27Nm88sor5ObmUlhYyOzZs6mvr/d5rLcdgy5UdCECkJubS35+Pq+99hrXXXddxKeCz5gxg4ULFzJq1CgGDRpEt27dAJg+fToLFy5E0zQGDhxI7969I7rdQEhBpyDEEfWSFhGEiPPtt9/6/L5s2TIGDRrE5s2bKSkp4ZFHHmHatGkMHTrUp5gzFJKTk/nwww9JSkpi9uzZVFRURGLpBjNnzuSbb75h/vz5zJgxw7j9pJNOYtGiRSxatKjdohYg4kIQ4orGtEgHL0QQEojCwkLmzZvHli1beO2113j88ce5/fbbKSgowG638/jjj7Nz504++OADHnjggbC3k5qaykcffYTVamXOnDlUVlYG9byNGzeyZs0ajh49SllZGWvWrGHNmjU+j9HrLv71r38ZtR+gIhfffvsty5cvb1dxEd8xK0HoZEjkQhAiz1VXXUVNTQ0TJ07EYrFw++23c9NNN2EymXjhhRf45S9/yWOPPcbYsWP505/+xDnnnBP2ttLS0vjkk0+YPXs2Z555Jh9//DGpqaktPueMM85gz549xu9jxowB8Glj7devH3369GHPnj0+4qKgoID8/Hx2797tE9GINiYt1IbgNlJeXk5mZiZlZWURzzkJQqIz8cHPKa6oY96pg/nJKYM6ejmCYFBbW8uuXbvo169fm0Z1Cx1LS//HUI7fkhYRhDhCCjoFQYgHRFwIQhyhp0XaOeAoCEKUmDNnDmlpaX5/HnrooY5eXthIzYUgxBENLiUqXCIuBCEhePbZZ6mpqfF7X3Z2djuvJnKIuBCEOEHTNOkWEYQEo2fPnh29hKggaRFBiBP0qAVIt4ggCLGNiAtBiBPqvcIVUtApCEIsI+JCEOIEvZgTRFwIghDbiLgQhDihwStyIWkRQRBiGREXghAnSORCEIR4QcSFIMQJ9RK5EISIM2PGjIiPQBdEXAhC3CCRC0FIPL788kvOPvts8vPzMZlMvPfeez7333XXXQwdOtTnts2bN2Mymbjmmmt8bn/hhRdwOBwBfTPaExEXghAnNPh0i3TgQgRBiBhVVVWMHj2aJ5980u/9M2fOZMuWLRw8eNC4beHChfTu3ZtFixb5PHbhwoVMnjyZ5OTkaC45KERcCEKc4B25kLSIEPNoGtRXdcxPiN8Pp9PJbbfdRmZmJl27duU3v/mNYbH/0ksvMX78eNLT0+nevTuXX345xcXFPs9fvHgxEydOxOFw0KNHD+666y6cTmdQ254zZw6/+93v+MEPfuD3/hNPPBGbzeYjJBYtWsTcuXM5evQou3fv9rm9Pceqt4Q4dApCnCA+F0Jc0VAND+V3zLZ/uR/sLY8x9+bFF1/k+uuvZ/ny5axcuZKbbrqJgoICbrzxRhoaGnjggQcYMmQIxcXFzJs3j2uuuYaPP/4YgH379nHGGWdwzTXX8O9//5vNmzdz4403kpSUxL333tvmPyU1NZUJEyawcOFCLr30UkCJiDvvvJNFixaxcOFCrr32Wnbu3ElhYaGIC0EQQsOn5kIiF4IQMXr37s2jjz6KyWRiyJAhrFu3jkcffZQbb7yR6667znhc//79eeyxx5gwYQKVlZWkpaXx1FNP0bt3b5544glMJhNDhw5l//79/OIXv+Cee+7BbG57gmDmzJm8+eabAGzcuJHa2lrGjBnDSSedxKJFi7j22mtZtGgRSUlJTJ48uc3biwQiLgQhTvBJi0jkQoh1bCkqgtBR2w6ByZMnYzKZjN+nTJnCn//8Z1wuF2vWrOHee+9l7dq1HDt2DLdbfQ8LCwsZNmwYmzZtYsqUKT7Pnzp1KpWVlezdu5eCgoI2/zkzZszgwQcf5MCBAyxatIgTTzwRi8XC9OnT+fvf/w6oaMYJJ5yAw+Fo8/YigYgLQYgTvGeLSFpEiHlMppBSE7FIbW0ts2fPZvbs2bzyyivk5uZSWFjI7Nmzqa+vb7d1TJ06FbvdzsKFC1m4cCHTp08HYMKECRw5coSdO3eyaNEibr755nZbU2tIQacgxAn1LpdxXQo6BSFyfPvttz6/L1u2jEGDBrF582ZKSkp45JFHmDZtGkOHDm1WzHncccexdOlSowAU4OuvvyY9PZ1evXpFZH3JyclMmjSJRYsWsXjxYmbMmAGAzWZj8uTJPPfccxQVFcVMvQWIuBCEuKHB6T0VtQMXIggJRmFhIfPmzWPLli289tprPP7449x+++0UFBRgt9t5/PHH2blzJx988AEPPPCAz3N/9KMfUVRUxI9//GM2b97M+++/z29/+1vmzZsXVL1FZWUla9asYc2aNQDs2rWLNWvWUFhY6PO4mTNn8vrrr1NbW8vYsWON26dPn87jjz9uFH7GCiIuBCFOqJNuEUGICldddRU1NTVMnDiRuXPncvvtt3PTTTeRm5vLCy+8wJtvvsmwYcN45JFH+NOf/uTz3J49e/Lxxx+zfPlyRo8ezS233ML111/Pr3/966C2vXLlSsaMGcOYMWMAmDdvHmPGjOGee+7xedzMmTOpqKhg6tSpWK2NFQ3Tp0+noqLCaFmNFUya1r7x1fLycjIzMykrKyMjI6M9Ny0Icc2/luzi/g83AjBtUFdeun5SB69IEBqpra1l165d9OvXj6SkpI5ejhAmLf0fQzl+S+RCEOIE8bkQBCFeEHEhCHFCg8wWEYS4orCwkLS0tIA/TesqEglpRRWEOEGmogpCfJGfn28Uaga6P1ERcSEIcYJMRRWE+MJqtTJw4MCOXkaHIGkRQYgTfGouRFsIMUo79wgIESZS/z8RF4IQJ4j9txDLWCwWgHZ1rhQiT3V1NUCb21olLSIIcUKDdIsIMYzVaiUlJYXDhw9js9kiMrBLaD80TaO6upri4mKysrIMsRguIi4EIU7wiVxI6FmIMUwmEz169GDXrl3s2bOno5cjhElWVhbdu3dv8+uIuBCEOEEGlwmxjt1uZ9CgQZIaiVNsNlubIxY6IYkLl8vFvffey8svv8zBgwfJz8/nmmuu4de//rXPuFlBECJPnXe3iEQuhBjFbDaLQ6cQmrj4/e9/z9NPP82LL77I8OHDWblyJddeey2ZmZn85Cc/idYaBUGgic+FRC4EQYhhQhIX33zzDeeeey5nnnkmAH379uW1115j+fLlUVmcIAiNNEjkQhCEOCGkct4TTjiBBQsWsHXrVgDWrl3LkiVLmDNnTsDn1NXVUV5e7vMjCELo+EYuOnAhgiAIrRBS5OKuu+6ivLycoUOHYrFYcLlcPPjgg1xxxRUBn/Pwww9z3333tXmhgtDZaRD7b0EQ4oSQIhdvvPEGr7zyCq+++irfffcdL774In/605948cUXAz7n7rvvpqyszPgpKipq86IFoTMi9t+CIMQLIUUu7rzzTu666y4uvfRSAEaOHMmePXt4+OGHufrqq/0+x+Fw4HA42r5SQejkyOAyQRDihZAiF9XV1c1c1ywWC25JAAtC1JHIhSAI8UJIkYuzzz6bBx98kIKCAoYPH87q1av5y1/+wnXXXRet9QmC4EHsvwVBiBdCEhePP/44v/nNb/jRj35EcXEx+fn53Hzzzdxzzz3RWp8gCB587b87cCGCIAitEJK4SE9P569//St//etfo7QcQRACIWkRQRDiBRlbJwhxgs9sESnoFAQhhhFxIQhxgKZpYv8tCELcIOJCEOIA76gFSORCEITYRsSFIMQB3lELAE1T0QxBEIRYRMSFIMQB3kPLdKSoUxCEWEXEhSDEAU0jFyCpEUEQYhcRF4IQB9T7iVyIMa4gCLGKiAtBiAP0yIXd2viVlciFIAixiogLQYgD9MhFkre4kJoLQRBiFBEXghAH6HNFku0W4zbxuhAEIVYRcSEIcYARubB5iQtJiwiCEKOIuBCEOMCoubCYMZnUbVJzIQhCrCLiQhDiAD1yYbeasXjUhXSLCIIQq4i4EIQ4QLf/tlnMmM1KXEjkQhCEWEXEhSDEAf4jFyIuBEGITURcCEIcUO9yAarmwqJHLkRcCIIQo4i4EIQ4oMGphITdasYsBZ2CIMQ4Ii4EIQ6o83SL2CwmI3IhaRFBEGIVEReCEAc0GDUXlsa0iEQuBEGIUURcCEIc4O1zYTZJzYUgCLGNiAtBiAMaIxfeaZGOXJEgCEJgRFwIQhzgN3IhaRFBEGIUEReCEAfUGwWd0ooqCELsI+JCEOIAHxMtPS0ikQtBEGIUEReCEAfo4sJm8fK5kMiFIAgxiogLQYgDGlyNkQuz2H8LghDjiLgQhDhAj1w4fNIiHbkiQRCEwIi4EIQ4wGcqqnSLCIIQ44i4EIQ4oM5fQaeELgRBiFFEXAhCHNDg1YpqllZUQRBiHBEXghAH+LSiylRUQRBiHBEXghAHNDp0ylRUQRBiHxEXghAH+GtFlciFIAixiogLQYgDvE20xP5bEIRYR8SFIMQB3oPLxP5bEIRYR8SFIMQB3gWdRlpERq4LghCjiLgQhDigwc9UVCnoFAQhVhFxIQhxgLf9txR0CoIQ64i4EIQ4wNv+2+L51kpBpyAIsYqIC0GIA+r92X9L5EIQhBglJHHRt29fTCZTs5+5c+dGa32C0OnRNM3oFvEZXCaRC0EQYhRrKA9esWIFLpfL+H39+vWceuqpXHTRRRFfmCAICj0lAk27RURcCIIQm4QkLnJzc31+f+SRRxgwYADTp0+P6KIEQWik3qvn1CFpEUEQ4oCQxIU39fX1vPzyy8ybNw+T50zKH3V1ddTV1Rm/l5eXh7tJQeiUNDgbxYV3WkQCF4IgxCphF3S+9957lJaWcs0117T4uIcffpjMzEzjp3fv3uFuUhA6JXrkwmI2eX7U7ZIWEQQhVglbXDz33HPMmTOH/Pz8Fh939913U1ZWZvwUFRWFu0lB6JQ0zhVREQsx0RIEIdYJKy2yZ88ePv/8c955551WH+twOHA4HOFsRhAEfOeKAGKiJQhCzBNW5OL555+nW7dunHnmmZFejyAITfD2uACJXAiCEPuELC7cbjfPP/88V199NVZr2PWggiAESYNELgRBiDNCFheff/45hYWFXHfdddFYjyAITTBqLppELmQqqiAIsUrIoYfTTjsNTc6YBKHdaFpzIT4XgiDEOjJbRBBinKY1F+LQKQhCrCPiQhBiHO+JqID4XAiCEPOIuBCEGKdZt4hJ0iKCIMQ2Ii4EIcZp1i1iDjItUl8NlYf93+esj9j6BEEQmiLiQhBinLAiF243PH86/Gkg/GMmbJuvbq84CC+eA48UwIpnQaIfgiBEATGqEIQYp87la/8dVORi6ydwYK26vv87ePViGH0ZbP0fVJeo2z/6GSz6PdhT4cJ/Qc+xUfsbBEHoXEjkQhBinAYjcmEBvLtFWnjSN4+rywk3wJgrQXPDmleUsOg2HKb9HExmqCqGY7vg7euhrjKaf4YgCJ0IiVwIQozT3OdC3R4wLbJ3JRQuBbMNTroT0vKg6xAo+hZGnA9DzwarXQmP8n3wxtVwdCd8+FM472mwyG5BEIS2IXsRQYhxGiMXnrRIazUXa19TlyMvhPTu6vrUnzR/XEYP9fODv8OLZ8O6N1Rk46IXICkjkn+CIAidDEmLCEKME8ih02/NhabB5o/V9eHnB7eBftPg4hfBlgI7FsBnv2rzmgVB6NyIuBCEGKfeKOgMwv57/2qo2A+2VOh3UvAbGXYuXP6Guv7dS+p1BEEQwkTEhSDEOCHZf2/xRC0GngK2pNA21G8ajLwI0OC/d0D10TBXLAhCZ0fEhSDEOA0BIhd+u0X0lMjQs8Lb2Kn3gz0dDqyBZ06CQxvDex1BEDo1Ii4EIcYJ2kTL1QDFG9T1/jPC21hGPlz7MWT3h7Ii+M8VUFcR3msJgtBpEXEhCDGOIS5as//W0xgmM6Tmhr/BHqPghgWQ2buxRVUQBCEERFwIQoyjT0U1IheBfC5qPOIiKQvMbfxqp2Qr106TBda9CcWb2vZ6giB0KkRcCEKMUxdsQaceuUjJjsyGe0+EgbPU9U3/jcxrCoLQKRBxIQgxTuCCzgCRi+QIiQuAYeeoy40fRO41BUFIeERcCEKME3RBpz6QLCUnchsfcoZKjRxap+ovBEEQgkDEhSDEOI0Ona1MRY10WkR/rb4nquvr3o7c6wqCkNCIuBCEGKeqzglAqkONAtIjF66mHlpGWqRLZBcw8iJ1+eUf1VA0b9wu2PY5rHoRakrh68fg6alqnPvhLZFdhyAIcYMMLhOEGKfSIy7SdHGh2383i1wcU5eRjFwAHH8FbPkEtnwEr18OV7wFzjo1IG3LJ8puHOCTX4CzRl0/tB7WvwN3rANHWmTXIwhCzCORC0GIcZqKi4BpkWgUdKoNwvnPQN4IqDwE/5wJz82Clc8pYZGUBV36KmFhtsL0uyCzQK1n3RuRXYsgCHGBRC4EIYbRNM1Ii6QlecSF0hZ+CjqjUHOh40iHaz6Et66DHV8oETHyYhh+HvSbroy71r8NuUOg51hIyoRP74bl/4Rx14InlSMIQudAxIUgxDB1TrdhopXWtOaiWVokCt0i3iR3gcvfhO3zIW84ZBX43n/8ZV7XL4cvHoDijbB7iRqKJghCp0HSIoIQw+gpEYBUu29aJKBDZ6TTIkBxRS2apoHFCkPmNBcWTUnOUmPcAXYuDPw4V4OqzTi2J2JrFQSh40kcceFywsH1Hb0KQYgoRqeI3WKICqOg01tbuN1QE52CzgWbDjHxwQU8uXB7aE/sOlhdlu3zf7+mqbklb10LT5+g0iqCICQEiSEunHXwh/7w96mBd2SCEIdU1PrWW0AA+++6MtA8M9gjHLnYeqgSgE0HQ5yOmtlbXZbt9X//N4/D6pfU9frKxnoOQRDinsQQF1YHdOmjru9d3rFrEYQIUtnE4wIC2H/rxZz2NLDaI7oGp8fEq7beFdoTM3uqy3I/4mLLJzD/HnX9tAdVuyuodlZXQ5grFQQhVkgMcQHQe5K6LFrRsesQhAiip0XSvcWFP/tvPSUShXqLBo+IqWkIVVz0Updl+1TaRufQBnj7BkBTnSRT5sLshyClKxzZCot/7/t4QRDijgQSFxPVZdG3HbsOQYgg/iIX+jR138iF3ikSeXGhRy5CFhfpPQATuBug6rC6reoIvHapSoP0nQZn/FG1qSZnwSmeSMaXf4TnTlUiRBCEuCTxxMWBtdBQ07FrEYQIYdRc+EmL+EQuouhx4dQjF6GmRSw2j8Cgse7irWuhtBCy+8PF/1aP0Rl7lYpg2NNg30p4Zjos+j046yPwVwiC0J4kjrjI6gNpeeosaf+ajl6NIESEpgZaEMDnIoptqPrI9zpnGKkK77qLQxth15dgscNl/2kuhEwmlSK5bSUMOVN9lxc9pBxBi6SWShDiicQRFyaTpEaEhKOp9Tc0Ri6c3uKi6oi6jEpaJMzIBXjVXeyFDe+q6wNnQe7gwM/J6AGXvgIXPKfE0qH1Kk3y6a9C374gCB1C4ogLgF4ecbFXijqFxMCfuNDrL6rrXcrYChonkHbpF/E1ON1h1lwAZHgiF2X7GsXF8PNbf57JBCMvhLnL4fgr1W1Ln4BN/w19DYIgtDuJJS6MjpFvlUGPIMQ5lX58LnRx4XJr1DZ4UhUHv1eXPUZFfA26/XhY4kL3utj6PyjZBhYHDDk9+Oen5cJ5T8K0n6nfP/pZY32JIAgxS2KJix6jVT636jAc29XRqxGENuMvcpFis/jeX30UyorUDd1HRnwNerdIvdPdfMx7a+g1F0d3qMtBp6ohaKFy0v9BziDPVNaTpeVcEGKcxBIXtiTocby6LgVgQgLgT1yYzSbj96o6Jxxcp+7I6qOmkUaYBi9BUesM0+sCwGyDE+eFtwhbElz0vIqEHNsF/5oNix5Rtv+CIMQciSUuwKuoU8SFEP/4ExcAqQ5L4/1RTIlAY+QCwijqzPQacDb7Ieg1LvyFdB8JtyyBkReB5oJFD8Nb14ijpyDEICGLi3379nHllVeSk5NDcnIyI0eOZOXKldFYW3iIuBASiEo/Phfev1fWOeGAR1x0Hx2VNejdIhBG3UVqDpz+iLL4nnhj2xeTnAUXPAvn/1OlQDf9F965USIYghBjWFt/SCPHjh1j6tSpzJw5k08++YTc3Fy2bdtGly5dorW+0NE7Roo3QG05JGV07HoEoQ3487kAL3FR65UWiUK9BTRJi4RT1Dn51giuxsOoi1UK6PUrVBeK2Qo/eAbMltafKwhC1AlJXPz+97+nd+/ePP/888Zt/fpFvvWtTWT0UKHYskLYtwoGzOzoFQlC2FQETIuo32tqKtU8DmiXtIjRnRILDJ4NF78Ib1wF695UBZ8zftHRqxIEgRDTIh988AHjx4/noosuolu3bowZM4Z//vOf0Vpb+PQWvwsh/tE0rTFyESAt4ij+XtUfpHZrtNqOMG1Ki0SboWeq+SQAa1+TFnRBiBFCEhc7d+7k6aefZtCgQXz66afceuut/OQnP+HFF18M+Jy6ujrKy8t9fqKOt9+FIMQpNQ0u9IxEoLRI+pE16obeE5XxVBRocLehoLM9GHmR6kQ5tgtKtnf0agRBIERx4Xa7GTt2LA899BBjxozhpptu4sYbb+Tvf/97wOc8/PDDZGZmGj+9e/du86JbxSjqXCGjm4W4RS/mNJsg2eZbS6CLja7H1qobek2I2jpiOnIByjej71R1feunHbsWQRCAEMVFjx49GDZsmM9txx13HIWFhQGfc/fdd1NWVmb8FBUVhbfSUMgbAbYUqCuDI1uivz1BiAIVXuPWTU2iEqrmQqN7haeYM4riosGn5iIGxQXAYI/r59b/dew6BEEAQhQXU6dOZcsW34P11q1b6dOnT8DnOBwOMjIyfH6ijsUKPT399NKSKsQper1FuqN53XWaw0o+JaQ3HFGdEvljorYOZ1u7RdqDQaepy8KlUL6/Y9ciCEJo4uKnP/0py5Yt46GHHmL79u28+uqr/OMf/2Du3LnRWl/4iN+FEOfoaZHUAOJijNlTX5A3AuwpUVtHm0y02oucAcr+3+2El34AVSUdvSJB6NSEJC4mTJjAu+++y2uvvcaIESN44IEH+Otf/8oVV1wRrfWFj0xIFeKcKs+BPMWPuEh1WBlr3qZ+iWJKBBoHlwHUxFIralMu/rfqmDm8GV4+H2rLOnpFgtBpCcnnAuCss87irLPOisZaIove81+yDRpqwJbcsesRhBCpd6oDucPS/BwgzWGlvy4u9ChdlHB6d4vEaloEoEtfuOp9eH4OHFgDr14CV7wFjrSOXpkgdDoSb7aITnoPSM4GzQ3Fmzp6NYIQMvpB3WZt3mKabnUx3LRb/dJrfHTX4RW5qItlcQGQOwR++C44MlX9xcsXKKdeQRDalcQVFyZTox3yofUduxZBCAM9HWE1N/+adq3chMPk5BgZ0CW6Lrne3SIxHbnQ6TFaCYykTChaBi+dBzXHOnpVgtCpSFxxAY3iQp+9IAhxhH5Qt/lJi2QfU8PK1jIoauZZOt7dIjFb0NmUXuPgqg8guYsaA/DiOVBX0dGrEoROQycRFxK5EOIPpyEumouHtOLvAFjpHIgWZcvrmDfRCkT+8XDNR5DSVY2l/+6ljl6RIHQaEltc5I1Ql4fWy8wBIe4w0iJ+IheOQx5x4R5EnTO6HRze9t8xNbgsGPKGw/T/U9c3vNOxaxGETkRii4uug9XMgbpyKN3T0asRhJBoCBS5KC3CXL4Pl2bie3d/Kj1mW9HA5dZ8dHnMmmi1xLDzwGRWbenHZD8gCO1BYosLqx1yh6rrhzZ27FoEIUT0Wgdb04LOje8BsJohVJNkOHlGA+9iToiztIhOeh708cwe2fBux65FEDoJiS0uADJ7qsuqwx27DkEIEd3nolkr6rq3AFhgmQZARW30xIV3MSfEUUFnU0ZcoC5XPAtHd3XsWgShE5D44iIpS13WlnbkKgQhZHSfC59W1CPblUGUycKyZCUuohm5cDaJXMRlWgRg+A8gszeUFcGzp8COLzp6RYKQ0CS+uEjOUpc1pR25CkEIGb1Lw6fmYr2KWjDgZFzJOQBU1UczLeIbuYhbcZGcBdfPVx4Y1SVq/sj/fgkNtR29MkFISBJfXCRlqkuJXAhxRr0/n4sdC9XlsHNJtSv3/uimRRKg5kInowdc+z+YcIP6fdmT8M+TYf+aDl2WICQinUBcZKlLiVwIcYazaSuqywkH1qrrBZONaalVddE74DubRC7iWlyAmh575p/hsv8o/4viDfDPmbDwoY5emSAkFIkvLvS0iEQuhDhD79Sw62mRw5vAWQOODMgeQHqSEheVdQ1RX4NObYM76qZd7cKQ0+FHS1Whp+aGxb+HPUs7elWCkDAkvriQyEXC0+Byc+Wz3/L7/23u6KVElGYmWvuUcRb5x4PZTFaKDYCSyvqorUHvFnFYG3cV0TbtajfSusGF/4KxV6vfF0n0QhAiReKLC4lcJDxbD1WwZPsRXlmWWAZJjd0insjFfl1cjAWge0YSAAfLo1eUqEcu9CgJxHE7aiBOulOZ7e36Elb+C5x1Hb0iQYh7El9cGK2oZR26DCF6VHsOdlX1rsQI2Xsw0iJ61GDfKnXZ0yMuMj3ioix64kKvuXBYLdg9EZS4r7toSlZvGHeNuv7hT+Evx8Gnv4LtC6DiILgT7O8VhHbA2vpD4hzvVlRNi/oESaH90X0eXG6NOqebJJulg1cUGXxGrjfUNLrM9hwHNEYuDkUxcmFETywmkmxm6l3uxBMXAKf9DlKy1XCziv2w9An1A2C2wpgr4fTfgy2pY9cpCHFC54lcaC6or+zQpQjRodorTB/NORvtjc9U1G8eV5/htDzIUK6zRuSivDZqEZtGgWMiPUnVeJTVRK+AtMOwJcHMX8Id61QnyciLIHuAmknidsKqF+C5U2HXVx29UkGICxI/cmFLBosdXPUqeuFI7+gVCRHG26Gyqs5J1zRHB64mcugH9sF7XoN1D6obp/+fEX3L80QuahvclNc4yfQUeEaSRiMvMxnJNvaV1lBcnsA1CRar6iQZcrr63eWEXYvg7RvV2PYXz4IhZ8J5TzVGRQVBaEbiRy5MJrEAT3ASNXLR4HRxm+VdRuvC4qQ7Gw2ggCSbxegYiVZRZ4NXWqRbuhJthys6kaulxQoDZ8GtX6v33mKHLR8pb4wj2zp6dYIQsyS+uACxAE9wvO2vo2ko1a4c3sKvSu7i57Y31e8n3Qkzf9XsYdHuGHF61X10S1fbKq5I4MhFIDLylfnW9Z+pGSVHd8Jzp0HR8o5emSDEJAkjLr7ZcYRPNxz0f+YqFuAJTbWXoIjmEK92ob4a3rgKnpzEqIa11Go2toz7LZz8a7/FyHpq5FCUOka86z70yEVCp0VaI38M3LhQtQPXHIV/nwe7v+7oVQlCzJEw4uLHr67m5pdWse9YTfM7xUgrofGOXMR9WmTFs7DxfUDjG9tkTq3/A4eG/DDgw6MduWhwe0UuMjziIsS0yHur9/HR9wcivrYOIy0XrvkQ+s+Ehip45UKZTyIITUgYcZFsV+2HfidEipFWQpNQkYttn6nL2Q9xT/IvKdLyfAeXNSEvM9ppEe+ai9DTIhW1Dcx7Yw0/eX015bUJ1GViT4XLXocBJ0NDNXx8p2p1FwQBSCBxoU+IrPaXc5fIRUKTMJGL2jIo9My3GHKGbytqAIzIRdTSIo3dIrl6WiQEcXG0qh63pjxINu0vj8oaOwxbEpz7FNhSYe9yWP9243215bB3Jax+Gb78Eyz6PWz+GOoqOm69gtCOJEwraopDRS6qJXLR6fDuFolEQeexqnpuemklI3tmcc/Zw9r8ekGzY6HyVMgZBNn9aHDtAmgxctE9Ux3woyUuGrwsyPW0SEllHS63hsXcuiFdaXVjtGLD/nIm9c+Jyjo7jIweMPV2NZfk7evho3kqglEXQEiZbVAwGYafp7w09HowQUgwEiZykWLXxYVELjobPj4X/sRliNz88ipW7D7Gv77e1ebXColt89XloNOARvtva4uRi2Qgei6d3pGLnFQHZhO4NSUwguFYdeNQtQ2JFrnQOeHHKj0CKvqkC4u07tB/hnL3PP4K6NIX3A2w+yv46Gfw15Gwf3VHrVoQokriRC70tIg/cSGRi4Qmkj4XCzYdYvmuo8bvwZ6ht5mSHbDpv+r6oFOBxomkLUcuVFqkpKqeOqcLhzWy1ufeAsdiNtE1zUFxRR3FFXV0y2jdCtvbzXPD/gSd72NPgR++C/VVUFqoohOpOZDcpfljS3bAlk9g1fNQsh3+cxXcvFhZjwtCApGAkQt/rahZ6lKGlyUkvj4XbRMX/1lR5PN7u9Rw1ByDVy+GujI1N6TvNAAanHrNReCvaZcUmzEO/VBZ5FtEnV7dIoCRGnl1eSEPfrQRl7vlIkbvtMj24kou/+cyzn1iCbWJOJ/EngrdjoOuA/0LC4CcAXDCbXDDAujSD8oKla346pfBWe//OYIQhySQuAgiclFzrP0WJLQbkewWaZpWqYh2h4OrQflalGyHjF5w6WvKFRLfeodAmEwmemap1Mi+Uj9t2G2kaVGp3jHy6reF/POrXSzdUdLi873FhdOt8c2OEtbuLePr7Ucivta4IjkLLnlZiZCS7fD+XPjbKFj+T5nCKiQECSQuWmhFzeytLo/tViZFQkIRyW6Rek+0QKeiNoqRi8NbVRHgri/BngaX/wfS84y79XoHY+R6API94mK/R1xU1jn515JdHChru9gwBpcZ4sJ3bkuds+UDoXfNhTfzNx5q89rinu4j4Pa1MOs+VZ9RcQA+/jk8ewps/1xaW4W4JmHERapHXNT4i1xkFUB6vqrE37eqnVcmRBNN0yLaLdJUXEQ8LaJpyjL61UvgyQkewywTXPCcOtgYD9O8UhIt13z08NRd6GLine/2cv+HG3lsQdtnXxgj1z1pkZw0u8/9dU3er6boNRdpDhWNyfOkVT7fdKjVlEqnICkTTrwD7vgezvgTODJVkefLF8C/zwVnJ3ZDFeKahBEXyZ60iN+Di8mk2r8ACpe146qEaFPndPscpNqaFulet4sfW97BgTrjbnNapL4K1r4OL18Iz54KT01WOfat/1PjvAfPgas/aJzC6UGPGABYW6i5gMbIxb5S1TGy33O560hV29aOd7eIEjh2i2/BaGu1E6WeyMVPThnIL88YyvtzTyQ9ycqRynrWFEma0sDqgIk3wm0rYPJcsKXArsXwv7s7emWCEBYJ0y2S2pLPBUDBFNjwTqNJkZAQNK2xaWuk4bbqJxlp24TVrPFowwXhpUU0DfZ8A2tehY3vQX2l7/3WJBhxIUybpwr8/KB3aQDYWxEXPZukRfQD+oEIeF80pkXUGi6fVMA3O47wraejpqY1ceGJXPTJSWX28O4AnDy0G++v2c9TC3fw+OUZRr2UgEqLnf4QDJgJr1wEK5+DqmKYdAv0PbGjVycIQZMwkYsWCzqhMXJRtFwKphKIppGKNkUuKg8z3LUZgMstX2DFSXk44uLDO+CFM2DNy0pYdOkLM34JF78EFz4PP9sM5z0ZUFhAY8QAWva5AOiR5ZsWOeYlLrQ25u31tIjNk5rJTXfwn5uncM7ofABqG1pOi+gFnV1SGtMpl00swGI2sWBzMRc8vbTVuo1OyaBT4ZTfqOub/gsvnAn/+aFqdRWEOCCBxEUrkYu84eDIgPoKOLS+HVcmRBNdTOplCVX1Ltzh5vK3fYoZ9dxcjnGqeRWVoYqLA2th1QuASZknXfsJ/GQNzPgFDDsHRpwfuE3RC71TBFqvuTDSIsdq0DSNY54Der3TTUlV29obm0YudJJs6vdg0yJZKTbjtsn9c3j1hklkJFnZdKCclbslPeKXaT+DW7+BcdeCyQKbPoAnJsLiP4Irjm3uhU5BAoqLADs7swV6T1TXdy9pp1UJ0UbvFMlJa+xiqA7XQ2HLJwCUaOkAXGWZH3rNxcKH1OXIC+HcJ6HPCX5HpbdGg1cLqKmV5+dnKnFRVe+ivNbJMS9B0VZbcGcAl9Bkm/q+1bXwXrvdmlHQmZVs87lvUv8cThzUFYD1+8R/JiB5w+Hsv8ItS5T/ibMGFv4Onp8DZfs6enWCEJAEEhetpEWg0aJXnzwpxD26x0VOqr0xehFOaqSuAnZ8AcBdDTfiwswUy0bSjm4I7vluNyz5q6dQ0wLT7wp9DV7oaRG9S6Mlku0WungiAwfKaozIBTTWYYS9Dt0l1Nw0cqHERW2AbpEdhyvZfrgSPYiUmWJr9pjh+WquxvpEtQWPJHnD4Or/wvn/VBHYvcvhvVs7elWCEJAEEhetpEUABs1Wl7u/lumECYIeuUh1WEn1tDuGXNRZ+C08dQI0VLNfy2G+exwbspUF9+QDL7f+/IpD8PL58Plv1e+Tb1UujW2gPoiJqN54p0ZKvbwl2lrUGWi+icMWuPW7uLyWsx5bwmmPfgmo76Y/W/Lh+RlAAtuCRxqTCUZdDDd+AWar6iYpWt7RqxIEvySeuGjJ56DrQMgeoIYH7VjYTivrpBzdCTsXRX0zuphMsVsML4WQIhfOenjrOigrRMvsxU/q5wImNva7BoDjKxYp87VAbJsPT58AOxeCNRnO+iuc9rsw/pImy/IaGBYMPTypkW3FlUa0AWB/G420nGHUXKzYfcyni6RpSkRHj1zsOlLV5hbiTkXXQTD6UnX9yz917FoEIQAhiYt7770Xk8nk8zN06NBorS0k9LPW6gZXyxXygz3Ri62fRmzbf/lsCy9+sztirxf3VBbDs7OUCdDOxdHdlEdMptrDjFysfwvK90JaHnU3fs1KTX2eTd1HsNg1CjNu+OAnzTuMnHXwv1/CKxdC9RHoNhxuWgTjrw2rxqIpwUxE9aanp2Ok6eTRA6VtrLlo0i2ik2QNnBZp6l+RmWJv9hhQnSd5GQ40DTYdkNRISJw4T/mkbPtUWYe3JIAFoQMIucF8+PDhfP75540vYI2NHvVkT+TC5daoc7qNnHAzBs+GZU+pL6XbpQo920DR0Woe+2I7AKcOyzPC050WTVMH42rPzImv/gT9p0dtc9UeIZHisBjiImiXTr1OAmDyrdRbU427slMd3O/8IR9Zfk3SrsXwzHQ1WOysRyFvhPIgOPi9evDEm+HU+8HW+pTQYGks6AxO//fqkgLAur2lPre31QI8ULeI/n3zF7lYW+Sb5ggUuQAYkZ/JofJi1u8rY3xfmQwaNDkDVF3PoofU0LPVL6uo7MgLYcgcKN8P838LzlroNR56TYReE6DHKGXYJQhRJmRlYLVa6d69ezTW0iZSvMREdb0rsLgoOEG1AlYdVl0jbTzweZ8lf7B2P7dMD+xd0CnY8C5s/QQsdtDcam7G3pVqBxcFquobIxdpHiO1oEPsO76AI1tUgdz463ysv7NTbezQevIH2y3c0/A3OLRO3TH/t0pcHPwekrPhvKfUzjzCBDNu3ZuCHCUudpeo2Tkmk9J5+yMVuWgSQQmUFnG63Kxr0v2R5aeYU2d4z0wWbC6Wos5wmPELtf9acL9yHj66Axb/Xv14U1akvpegZthc+DwMPq391yt0KkKuudi2bRv5+fn079+fK664gsLClk1d6urqKC8v9/mJBlaL2Rjw1GJRp9UOx52jrq9/u83b9e5OeW91nLaGaRocXAef/gqenAzf/iP819FzwCfOg1GevPDrV8CXf1QH8+LNUHUkYkOZfCIXugV8S/9/b9a/pS5HXQJJmYa4sFvMZCSpA+I7rhPh/GfhtAeVJfOh9fD96+p5V74VFWEBvq2owVCQneLze98cFYU5VF7bphkeDQG6Voy0SBNxseVQBTUNLqP+BRrni/hjQK5a575jkZ/o2ikomAzXfgy/2K0+p4PnqPkkZitMvR2u+gBOuQeGnAEpXZWp239vV7b0ghBFQopcTJo0iRdeeIEhQ4Zw4MAB7rvvPqZNm8b69etJT0/3+5yHH36Y++67LyKLbY1Uu4V6p7vldlSAERfAdy+qoVFn/EkJjjDxrpbffLCCJxduZ/rgXEb0zAz7NduVo7uU859+Zg6w4D4YrQ64IbHtMyjeoM6OJt8CteWqor2sCL5oUuSY3AWGnAmnPQAp4YfDfSIXSerjHJRlt7MONn+kro84H2gcWma3mkn3iIuKWifayAuV10TpHljuEV6DToOe48Jed2sEOqgHoqm4GJyXxp6SKpxujSOVdeRlhJeyCeRzYbSiNnHo1FMio3tn8vV2lRorqQxs5GUU4QYrCAX/JGXAqIvUj9uthjTq+zU9OttQo0y4ygrh68dgpswtEaJHSJGLOXPmcNFFFzFq1Chmz57Nxx9/TGlpKW+88UbA59x9992UlZUZP0VFRW1edCCC8roA5dGflge1pYa3Qbg0jZL88dMtXPqPZfFjabzyX0pYWDwRnez+6uzmu3+H/lpLHlWX469V4qFLH/jxKjjnCRh6FnQd7HGnNEHNMWWP/eREFc0IE+9uEd1iOtCYbx+2L4C6ckjvAb2VNbze/qnEhfosudxaY+fD5FvV2gFO+r+w1xwMzhAjF6kOK129JpbmpDkMQdGWdtTG9EzTVlT/aZG1RaUAHN87i4fPH4ndauauMwIXfeviImQnVCEwZrP/EyZbMpx2v7r+9V+hZEe7LkvoXLSpFTUrK4vBgwezffv2gI9xOBxkZGT4/ESLxnbUVnZUZgsMV2errAnCx6AF9ANPz6xkLh7fC7NJ1WEcq2rjNM32QjcUO+9puOQlmHqH+v2rv8CTk5TVcDDsWaqGwlnsaqqjjtUBY38Il76iJj7+Yjf86oAyBOo6RNW+LHo47OUXl6uR1KkOK9mpHnERjOW1noMedp7aGYNPWiTFbjFMuYwDX3Z/uPjf8IN/QO8JYa85GEIt6ATo7RW96JJiaxzF3gYjrUARlGTDRMtXXOw5qsLtg/PSuWxiARvum83MId0Cvr4RbZJW1PZh2HnQf4Yq9HzvVpmzJESNNomLyspKduzYQY8ePSK1njaR4ggycgEw9ip1ufljKD8Q9jb1bR3XI50/XDjaOHsurWnbTId24dhuOLxZOUoOPEXdNupiSMmBmqPqvq/+HFx+dslf1OXxl0NGK58HWzL0Owkuel79vum/ULY35OUv33WUpTtLMJtgQt8uxnt/tDVh53KqbiGA4ecZN9d5xIXNqtqs9bNqn+Flw85RKaMo09ilEXxbax8fcWE3vC/2tyVy0UpapKbeNy2i243r225NHIXlTSKEj8mkIon2dCj6Fj78KTS0fXquIDQlJHHx85//nMWLF7N7926++eYbfvCDH2CxWLjsssuitb6Q0DtGgsrf5g1T4XDNpdq4wkQXF8melIxuc1xaHQeRi62eqEXB5MZhWrZkuOBZmHgTpOerWQbb5sOG92Dfd/5f5+B6FQExmeGEnwS//bzhal6C5lJnUa9drlpD9cmPNaVQfbTZ045W1fP2qr3c874aQHfpxAIGdks3IhdHq+pa3u7+1VBbpmpKejVGILwjF4BX3UX7/y8buzSC/4p6111kpdgjErkI1LWS5Ge2iKZpRgpG33ZrpHmdELSl8FQIgazecIYnIvndi/DMNFj7H3DFwT5LiBtCKujcu3cvl112GSUlJeTm5nLiiSeybNkycnNzo7W+kEh1BLYk9sv466BomfqCTfuZER4PhRo95+/Z2eo9/TEvLlxONWURGo3FdAacrH4sdlj6hOoiKd8LSVkwbxPYfYsHWf2SujzunBbHiPtl4k2w+yvVsgqw5SNlo507FEq2K9fL6z9TYtDDvDfWsGjLYQDSHVbmnToYoDEt0tp7r9fZ9J/h43PSWHOhbksPpUA0wjQ4Q2tFBSjI8fbpsNHD47nSlpoLw8yrqYmWXnPhlRY5Vt1gRH+6ZQTnpaCnRUCdFOhdOkKUOf4ySM2F926BI1vh3ZtUS+vUn8D461Ub+fevw8rnVbQjb7jqRBk4q00F8ELnISRx8frrr0drHREh2WhFDFJcDDsX3v+R6mao2A+ZvULeZmPkwiMuPKH5slhLi1QWqzP2ASeryMDrl6u0B6idhj+GnavERbknZVFbCuvehHFXNz5G04xpooy8KPR1DTkDjr9Ctaf2nqAcPXcvaVxbfQW8fb2ap2BTB8t1e1VHwunDu/PDKX3o6pmImp2qDkxHW6u50MWFPsjOg3e3CDSKi5BnlUQAfeR6a+PWvWkaucjPVK/RFgvwQDbkeitqg0vD5dawmE2GYVfXNLvfWSL+cFgt2CwmGlwalbUiLtqVQbNUHdSK5+Dbv6vv+Sf/B2teVbVQ5V6t9ftWqSLvASfDle9ExIVWSGxiw14zQqQGW9CpY0uCLn3VGXLJ9jaJixRDXKidY6tnz+2B2wVbPlY7i62fqvTDmCtVlfjhzcoE6rTfQe5g/8/vOV51U1QcAGuSKgJb/k9Vr6LvXA5vVi2aFgcMmBn6Gi1WZUSlc9Kdyl2wcJn6f7x+BRRvVO2y5z1FubULJR7x8KeLR/v4KXQxhF0DDS43NpOm0h+1pWpQndkGaLB3hXpCf9/16uLCEQNpkQaj/iO8tEiXFDtmz/+oLRbgenomUM0FqI6RVIe1Wb1FsKQ5rByrbugQEdfpSe4CJ/0cptymIpAL7ocDa9R9ad3hhNsgs7f6Pq58Tgnz/auh59gOXbYQ+ySUuNCjB9V+LIkDkjNICYsj21SYPERqmoqLZE9BZyyIiwX3q5Yzb/T6ElsK3Pylyr8GwmyGGXepM5s5f4CXfqDaVguXQp8T1GP0qEW/k8CeGvi1QiEj3/Ce4Pxn4NVLYPt8+NvxmLqN4yeWbnyXNNFHWIA6W9edKWu+ewPb/J+ptlp/5AxUrbJe1LvU/1KPXGR4IhdHWvBp0PnHlzv4ZkcJT10x1miJbguNo86DP0Pslu4gPzOJilon3TOSDLFdXFGL0+VuZuEdDIG6RRxeoqfGIy709Ev3IOstdFLjVFys31dGt3QH3cL0EIkpbEkw8UYYfDp8/TeVBhl9WaOl/fDz1AyddW8qESLiQmiFhJmKChgOjUFHLqCxRiDMnm9dyOgpGT1yERNpke0L1OXxV8Lc5b4totPmtSwsdMZdA7d8BX2mwMgL1G3r3mq8f+v/1OWQ0yOy5GYMOBluXAh5I6GhivR9XzLP9hb/dt3VbCiaxWwiK9lGH9NB0j69o1FY2FLVWVhKV1Ulb0tR80Ca0DQtMqZAFbl+9H3L3USapvH4F9tZtOUwX2493MY/WNEQ4lRUALPZxAc/PpFPf3oSyXYLOWkOrGYTbg0OVbRS5BqAQH4bZrPJEBi618XBEIs5deLR62J/aQ1nP7GEG/69sqOXElmyesOZf1JeNU1n5Yz5obpc9xbUV7f/2oS4IqHERYqnoDOoVlSdroPUZcm2sLZZ42XiBI3iosMjF/VVyi0T4ORfQe4QOOU3arZK78kw5cehv+Zx56rLLZ94rL7/qNrZMKkznmjRfYSKstz8FYsG/B/L3UPUtFK9/VWn4iCX2xbzjO1RzM4a1Yny62L41X74+Rb4vx3wy73KZ2PSTc0207Rb5Nzj87FbzGw8UM76JvMyvNl7rMYo+ly/LzL29o1TUUP7inZNcxjD8yxmk2GkdTDMuosGt//BZdDcpTPcyEVH1raEy4GyWjQNCo92ooNs32mQ1UeZz615paNXI8Q4iSUubGGIixxdXAQ2AmuJ5jUXIbhERpMDa1XFd3oPlWYAVRB53Sdw/afhTfDsd5Ky9q7YD+/e0mjpffKvw6pXCQmzGXqM4gP7mfy0/ke4McPORUroLPkr/GMm/HkId9Y9wVBzEQ22dGUMFsIEyLomkYusFDunDc8D4M2VRWiaxj3vr+e2V7/jUHljHcOG/WV+r7eFUB06A5HvGcUezgAzTdMazbz8pGeaDi87WK4ETKiRC32abTyJC70FtzrYCbyJgNkMk3+krs+/Bw5v7dj1CDFNYomLcOYU5AxUl6WFat5EiBjdIrHWirrXE66N5PwLW1Kj2ZY+vGvWfaogrJ3YVVLFPnI52NPTPvvapap1db/y4NjlGMqfGy7kwyn/CS7t44W3/bfOJRPUa7zz3T6e/3o3/166hw+/P8AZf/vKsLre6DXRM1LTPRtCnIoaCL24MpzR69X1LmO+nHfLqE5j5MLl2YYncpERekEnxFdaRBei9S63zzTdhGfiTdBvOjRUw1vXqkLptqJpUPgt7PpKtcgLCUFCiYuwdlJp3dTIbc2thniFSGNBZ9Oaiw4WF/tWqctID9cacmbj9aFnqcmL7cjuI8ottGbiXNX9YbKoQtyzHoWfbeWZwf/kcdf57NUCW04HQveW8BYXUwd0ZWTPTCrqnNz/4UZAeWuUVNXzy3fXoWkaG7wExeGKOorLQ48SNF+L/y6NUOnRhsiFnuqxmE2GePZGv23D/nJeWrrb+N+EW3MRTy6d3rODgvbVSQTMZvjBM8oj49B61cXlDDFKW7ZXnfy4GmDPN/D8HPjXafDiWfDnIbD+neisXWhXEqpbJMNrkmXQmEyqqHP/alV30S3wkCV/VDeobSXHWreI7qYZaXExeLayB7elwNmPtWu/e1l1g9Hi233oFOizWq0jNcd4THbqMQCjXTUUjG4Rr2iB2Wzi0UuO56zHv6K2wU1uuoN3f3QCpz36JRv2l7Noy2E2HlDiwmI24XIrsdHWDgK9W8TexshFvidysack9BHbegtuepJVTYVtgsMjLn77wQaf20OtuUiLx7SIV7Siqt5pOPN2CjJ6wOVvwAtnwc6F8MYP4aIXDB8avxStgG+fVhEK3TfHntZYdG1NUt/l6iMqIlJxEKb8KOp/ihA9Eipy0eioGOKBXa+7OBJ6UWezVlSPkVNNg6vZxMh2o7JYjVXGBPljIvvayVlq0umt3/gc1NuDXZ4DZLd0h8rTZ/VutoZGl84wxIXuc9HEW2JgtzQe+sFI0pOs/PbsYfTqksKVk1Ub628/2GCkA2YOUU61kai7qDecMdv2FZ3QV42zX7L9SOvmYk3QZ6qk+0mJACQF8OBI8hPlaIl4HF7m/d1uOhm5U9BzLFzybyUKtv4PHhsDz86CTR+q+zUNVjwL/71DDQn89zmw/m0lLEwWZb1fXwlmK4y7Fn6yGn6+DSbdop7/6d3w/Zsd9ucJbSehIhf6TrA81Nxt95Gw7g3Vvz35RyEVOzYt6Ex3WI0z2LKahpB3tBFh5yJ1mTcckqIwhVafQ9LO7PfMyPCe/tmUxuFl4YsLu5+D5vlje3H+2Mai1Rum9ePFb3Yb3QJ9clKY1C+HzzcVs66FzpJgCTQwLFSG5WcwomcG6/eV8+7qfVx/Yr+gn2tELhz+z8qbfrbTHFYunRBanYv+PIi3tIhX5KIzFXV6M3AW/PBdePVSZbRXcQD+cyVMvlUNRdzysXrcKs+Awn7T1ZiFnuNUlOPAGkjL8y0GP/0RVYT99d/gg9sgqwAKJrX3XyZEgISKXGR4iikr65yhDUEad43yQTi6E755LOinaZpmjFzX0yImk4nMji7q1L0nBp3WMduPEvrBJ9CZNOA1vCyctIhvK2pLdEtP4u9XjqNfV2UcNn1wLmMKsgBYuqPE6LIIF912u61pEYBLJhQA8J8VhWha8N+LitYiFzav9JEJ1txzKr8+a5jfx7ZEXBZ0NvimRTotfU6AO9bCNR+rWU1osOwpJSzMNtX2DiqCeumr0H86ONLUTJ+e45p3mZlMcMq9aiSBsxb+fS5s/KC9/yohAiRk5AKUwNAP8q2SlAGzH1QzLL76s5qRkd36GV5tg9uopvd2ZcxKtnG0qp7SjmhHdTlh++fqetOBZHGOLuT0KJE/jLRIGOKiaStqa8wc2o2TBuey9VAF/bqmYrOY6Zpm50hlPct2ljBtUPgD/eojFLkAOGd0Pg9+tJGthyrZfLCC43oEF81qFBf+v0feRZ55GUlhOYBCfKZFvAs6O1U7qj+Su0DfqUpo9ByvhhDaU9XMoF7j4Mh2lcIMti3cbFaTmd+6Vk1bfus6uGOdqvUQ4oaEilw4rBbjwBBy3cWIC5SPg7MWPvkFBHGG551r9d7RZnbkfJG9y9U8jeQuPuPEEwE9/Jxsaz1yUVJVH9JZOrScFgmExWziuB4ZJNksWMwmTh3WHYD31+znqUXb+XzjoZDWoKNHLsI9YHuTmWxjSHclKIpCMH3Sv0MZASMXjZ/5UDtEvEmN97RIZ45ceGMywZgrlGX/WX9RwgKg68CQ/GYAFd249DXoNhzcDWrkgBBXJJS4gMaOkfKaEL/wJhOc8WcVytv2KWz+sNWn6PUWDqsZi5fJUJeOnIyqp0QGnuozTjwR0N1QUx2B/65uGQ7MJrXzPxyi5XU44qIpp49Q4uKtVXv5w/+2cMO/V/Lr99aF3K7YYKRoItONkxNGuqj1tIiXuMgKzdvCm/Q47BbxLujstDUX0cZibZxhpPv2CHFDAoqLMDtGQE0HPcFji/3Bj5WxVgsECtN3mJFWTakaiwwwJMAY9Tim6Xh7fzisFmM66PbiAEPLAhBKzUUgpvTPMQ7GetfJy8sKmf3XL3nnu70cqQxO8Bi2223sFtExalFCSNU1tqL6T4s4vGou8jtx5KJTdou0F73Gq0t9krEQNyRUzQV4t6OG+YWf/gvVu71/tTKIuep91X7ph+omBlo6elqkNBJGWtVHYfdXsGcpHN4ElYfV6HS3S10C5I1Qsz32fwc1x6DrEDjunLZvO8bQh8SltJAWARjYLZ3dJdVsK67khIFdg379SEQu7FYzt0wfwDvf7eUPF46iss7FXW9/T+HRaua9sRa7xcw/rhrHjCEtm3wZ9t9tWIs3RuQiiAmvOq1GLqzeaZHwIxd6QWfY39kOwKegUyIX0UNP7R5Yq8y6rPaOXY8QNAknLvSOkfJwIheg2lAv/jc8M121Sj17ijKM0aeneqGfsTQ9k+6apvKLxeVNzlLdbtWudXSnEgEp2WpSp9mqDLwOb1YuoXUVqge84iAcXAe0UjtwdCds8qqoPu0BFVJMMPRpty2lRUD5Uny+6VDokYsAPhehMnfmQObOHGj8Pn/edP7x5U4+XneA7cWVPPLJZqYPzvVrTKXT0kyPcAini6a8tYJOr8+9PsMkHHTxUud00+Byt9nyvD3wKeiUyEX0yO6v6sdqjsGhdZE3BRSiRsIdgdocuQDVW33V+/DaZWqg2bu3wA3zmz2sqYGWTq8u6iyu6Fi1Egif/hKKNynh4AxjOmXXIarYNP94NYTMbAWTWZnRuBtg99eq1uLAGtXCFWMtqMt2lrBoy2FunNaPnLQQC7u8CCYtAkpcQBvSIhGKFuikOazMO3Uw10/txwmPLGDzwQoWbT3MzBaiFw0RLOgE6OJV6Bos3g6d/vA20WpL5EJPi4BKjejD/2KZWmlFbR9MJtWBsn0+7F0l4iKOSDxx4dAtwNuYkugxSk0PfWyM6sDY1/yD3XRomY5u8lR0tBoWPqSc6XRMFujSR0Usao5C1RE1MC1noBqLnjNQpWHsaeqy5/jWW7D6nQQz74bactUC1o6W3MFw19vfs7ukmne+28vffziOsQXhmXAF04oKXuLicHiRi2idOWem2LhsYgHPLtnFUwu3M6OF6IXTHZmpqDrRKOj0Fj492hC5sFnMOKxm6pxuKmrjQ1xIK2o70muCEhdrX1PdKPbUjl6REAQJJy4ykiOYv83sBcPPVxNAv/2HarHyIlDkQi8odJYfRFv7OiZQczj6nqiiIpYozSGIhhtnG9lTUsXuEtX+WFxRx6/fXc/Ht08L67X0gr+WWlEBBuSqnc/hijrKahqC9jsxai6iGJa/flo//r1sDyt2H+OFb3Zz7VT/fir6ELVICZ1w0iIVdS0XdHoP5+uaGn5ESm3DSl1lfdx0jEgrajsy8kJY+oSqKXvtUrjirdBbW4V2J/aTmyGi7wjDrrloyqSb1OX6t5u1Q+m51qYFnTmpdlLsFq62fIrJVQe9JsLYq1TdRrSERTtzrKqeV78tbPV9/nLrYQB6Z6uw+fbiytDcU73QI0Wt1VykJ9no7hkcFkpqJFQTrXDokZnMr844DoCHPt7Eej9W4ZqmGQfuttZ/6OR4Dv7hRC4C+Vx4m8SZ21gbEm8dI77dIhK5iCo5A+DKt1U0d9eX8NVfOnpFQhAkoLgIc75IIHqOgz4nqtqG505TkwDfuRl2fRWwoNNkMnF96tfcaPlI3TD1J21KVazbW8Y5Tyzh6+1Hwn6NSPP3L3fwy3fX8dLSPS0+bvFWteZLxvfGajZR73JzMMyR5MGmRaAxNbIjBHERrZqLplw1pQ+nDsujwaXx/Ne7m92/rbiSg+W12K1mhuVHJhqVnaYiFzUNrqA8NzRNM8RFWgBxceXkPtgtZs47Pr/N6zM6RuJEXPj6XMTHmuOa3hPhHM9ohq/+rGrYmvDe6n2s2H20nRcmBCLhxEVYY9db49KXlYOn5lJtod+/Di+exQUrr+Bc8xJSbV7CoaYU3rmJn9U8hsPkZE/eLBhyZps2/+G6/Xy/t4w3Vha17e+IIJsPVABwqAWhUO90s3SHEhczhnQzalHCGf8NwTl06ujiYltxRdCvH6lukdYwmUz80DNV9ZsdR5o5iX6+Sbl6njAgp1lULFxS7RYj3VNS1brXRk2Dy4gwBUqL9MlJZfU9p/LoJce3fX0SuRBaY/j5qmDd3QDv3qxq1TwUHa3mjv+s4fbXVnfgAgVvEk5cGJGLSHhM6CR3gQv/BTd8Aec/qwb0WJPJq9rC3+xPcUnR/VC2F5Y+CU9MgO//gxszf2y4mFcK7lde+W1Ad5rcfSS8g3I02O0RCC2JuO8Kj1FV76JrmoNhPTKMWpTCkuAtqL0JxqFTJ5yOkcaai+g7m07om43dYuZAWS27mvxfF2wqBuCU4/Iitj2TyRRS3YX+fzWblDAJRKrD2mJLbbCkx9nwMu+CTqm5aCdMJmUrntxF+V7M/61xV7FnH1lcURey7b8QHRJQXESoW8QfvcbBqIvgrEdh3kY+y7uBBs3CsJL58Ohw1XJaVQzZA/h4wvM86TqPwqO1FB2tNkyRwuGIx/ho55GqmPji1DvdxoyKlt7nDfvLARjfpwtms4k+OUpc7A5DXGiaZphotdaKCuF1jLRXWgTU3zCuj+qa8U53lVTW8V3hMQBOGdqy0VaoZIfQjqr/X9MiJB5aQ0+9xE1Bp1crqnSLtCMZ+XDe0+r6t0/Dji+Axs+N0904qVroWBJOXES0W6QlUrL5uMuVXN/wcxosnh7/7qNUV8iPlpHcfwoA/9twkGl/WMhf5m8Ne1N65KKi1hnWKPFIU3SsGr0ms6X3WY8a6Af6Pjmqi6PwaOgRmEATaAOhb3PvsRqf/HggXG7NSAO0h7gAmDowB4Cvt5cYt334/QE0DYb1yCC/DfM6/JGTFrxLZ2sGWpEmNc7mi0i3SAcyZA5MuFFd//j/wFnvE/EKea6UEBUST1xEo+YiAIcr6/jSPZpPZn0O/7cLbvkKxl0NVruRAtBpS72E9wCu3a3UK9Q2uPx2IESSXYcb19DS+7yjqbjwvCe7j4QeuQg0gTYQOal2slJsaBrsCCJ6Ue91sGg/caGsyb/ecYRD5bXU1Lt4cuF2AC6d2Dvi2zPG0QcxX6Q1j4tIE29pEW/BWtvgblNkUgiDk38NqbnK2XjZU1TWNUZQoxK1FkIm4cSFvjOsaXAZFsrRYo8nvN+jR76y8vaiVxdfcRGs10JTXG6No14FeDsPtywufvDUN5z1+BKW74pe1bS3wKmoC/xF1lMSjZELT83F0eqQ0zt60VySzXcCbSBMJhMDc4Ovu/ARF+1kPz2yZyY9s5KpqHVy1uNL+PFr31FcUUevLslcOqEg4tvTp/WGkhbJaKfIRVocRS40TfOJXEDj3BuhnUjOgln3qevfPE5ldWNhecRsCIQ2kXDiIs3LSjia0Yt6p5v9pcrKWz9oepNst3D+2J5keYaYHWo6ZyRIjlbV420L0VLk4pvtR9h0QNU56Hn7aLDzSOuRi6NV9UYKp7/H1Kp3dgomkzqAhJreaWxDDf5MOpR21DpX48EhUq6YrWG1mHnlhkkM7Z7O4Yo6PvcUcv501uCoRE9CGV7W3pGLeEqL1Ps5aZG6iw5g1MWQnA3VR8g6/K1xs6RFYoOEExdWi9mobo9meGyvp+4gxW4hN8C8jL9cfDxf/+JkQO00w9lxeqdEIHBKQdM0/uxV12GJYhHe7ibiwl8UQo8W9MxKNgRBks1imFu1lt5pit6iGIzHhU5jO2rwkQu71dwuBYw6fbum8s6PTuCB80Zw+aQCbps5kPPG9IzKtnSvi1AiF+0lLuKpoNM7aqGn6KTuogOw2GCYmv7c/+Bnxs0SuYgNEk5cgJdLZxQVrJ4SKchOafFglOqwGvnkg2Whm0cdrvQVF03bFnU+23iIVXsaoxXRNCPyXoMrQHW2Li4GeA7wOn09RZ03v7SKt1btDXqbgazWWyKUdlR9UJijAyZyptit/HByHx76wUh+PntIUGmfcNAjF4crWv8cVrRzQWdaHPlc6PUWJlNjulMiFx3E8PMBGHJsITbUZydiBopCm0hIcaF3jJRF0uuiCboRlL+USFPyMtXZenEYzpR65KKnp3Ngd0nzdtTqeif3/3ejz23R2knX1Ls40EQk+UuNGJ0iub7i4kczB9AzK5kjlfU88klzl71ANE5EDT0tsrukqtX6m/p2sP7uaAblpQOw6WCFj0+DP454RK3eYRJtDIfOODgw6G2oDqvZ8FzRIxdOl5uXlu7m7RCEs9AG+p4Iqd1IcVXwtO1RLrcsYNDmvysn5ZfOhxXPwv7VPoZbQvuQkHvS3HSVpjhSGb0PlO7VoJ+Jt0RehlpPOLbX+t8wpiALs0kdZPeV+o5tf2rhDvaV1tAzK5m5MwcA0RMXe4+pvzvdYTVmTvgVF02KOXWmDcrl3bknAKouI9g5I/rOuyVDp6bkZyZjNZtocGmtfhY6g7jo3zWVLik26p1uw4MkEHqULS8j/GmnoRCPaRGH1WLUilTXO9l2qILzn/6G37y/gZ+/tZbiICJEQhsxW+CkOwGYZVnNQ7bnmLz7KeWkvGMBfPQz+McM+Oso2P11mzZVU+/if+sP+BR/C4FJuKmoAN3S1Q6xJWvqtlLoMZEqCCZy4dlBhyMuvCMXI3tlsbaolCXbjnDpxMZugv9+vx+Au+YMNQ6i0UqLFHnERa/sFMprGiivdfqtbdHrMvRiTm/0rgW3ppxUu6S2fnYcTlrEbDbRJdXO4Yo6Sirr6ZEZ2Dei3lPQmcjiwmQyMa5PFz7fVMyq3ccYW9Al4GP1AuTu7SUu4igtokd9kmxm4/P458+2sq240jjwaBos33WUs0a1fe6K0AqTbuIn36YzvvhtupuO0j07k1FTz4D6Sti+AA6ug8qD8OLZcPxlMOQMSO8Oqd1UO6vFDntXwMb3YdtnqvOv74nqn7h/NRzdAQUnML96OE9vsLKjr5u5EzLBVQ+uBmVH7nZCej7kDISc/spFFKCqBGqOQn2V+mmohiPbYN2bartjr4JuQyEpS+XZti9Qrzn60mYdiPFGYooLT6Qg3A6NYNALEoOJXOg76EPh1Fx4xEVuuoOZQ3JZW1TKF5uLDXFR2+AyhM7k/jks2qI6DqIXuVBRk95dkin0pGf8RS50L4WufopdbRYzGUlWymudHK2uD0pchJMWAVVncLiirtUixpp6dVBIskbf+rsjGdcnW4mLPce4sYXH6cK83SIXcdQt4h250As69UjQjCG5ZCTZ+GDtfpbtLBFx0U5sduXzgfNaAM7K68ETE8eqO078qTqo//d2dUBf/bL68cbiAJfXsaIEKPrW9zGlhZwDnOMADgAftLIge5oqOK1ppWuvaJn/2xc+pARGwWQ4ugsOb4L6amUgNnCWciptx8LzcEhMceGJXEQrLOlya+w9qg6yTc2y/NE9s+2Ri9x0BxP7ZfPXz7exZPsRnv1qJzuPVHHphN5oGmSl2OiaZo/6GaAuLnp1SaG0WkUsmooLl7txomYgf4/sVDvltU6OVdVDbuvbrQ4jLQJerpStDOvSTXjaqzuio9Atx1cVHkPTNL/FyHVOlyHG9M9utNHTCw0ujTqnC0cMizy9oNNhNdOzi4qGpdgt3H/uCC4Y25PPNh7yiAuZ0Nle+Dh0Nj3ZsafC+f+EcdeqoZMH1kLVEagsVlEHVx04MtSBe+hZUH0EDnyvxEF2f8gZBDsWsG3VArrVF7FPy6VP3wGkpqSo6IPFBiazmi9Vsh0qDqioCQAmSMoAWyrYU8CWoqIax50NdeUqWlJVArWlKqrRa4ISQ4fWw4p/qh9vtn6iLpOzVdRj3DWQ3S9ab2ubSMg9qV7jUBylyMWBshrqXW5sFlNQFs2NaZrQ16OnObqmORiRn0nXNAdHKuv43UeqGLLWc0Y/qFsaJpPJyF1HqzBOr7no1SXZa3iZb1rEe2hcIHGRlWKHkuqg/S4aIxehHXSyU9VnoaQVb4fWxosnCqN6ZWKzmDhcUUfR0Rq/aT1d0NotZrqktG+3CKgDhSMtdsWFEbmwmblrznGcMjSPKQNySPJEMSb1y1YR7uJKjlTW+Y3eRZo/frqZT9Yf5K1bTjCcWDsT3mlgv0MrTSboO1X96Gga1JZBdQlk9gJrC/+nQbOYt20J6zzux3cPGMrN0wf4f2x9FZQfAGcNZA9QoiIQ037mux6TCdxu2LkQvv+PilrkDITcISr1svE9NW6+5ih8/Vf1k1kA/aapVE6P4+HwZji8BWbeHXi77UBC7kmjHbnQ7bX7d00Lqm1QP/sLpwZEb0XNTXdgNpuYOSSXN70q0T9cdwCAgd1UJ4AxujpKffeNkYtk4yy/qZDRu3RS7JaANQyhWFFDo7gIpeYCvIyjWhExejje+yCXiCTZLIzomcnqwlKW7SzxKy70z2m3DEe7eX5YzCZS7Baq611U1jnJaYcDcrjo3SJJVgtpDiszmwyYy0qxM7R7BpsOlPPtzqOcOapHVNez9VAFTy7cAcBX2w5z7vHR8UmJVTRN80mnBe1zYTIpp8/krKAeXuJVFL5gU3FgcWFPha4Dg1tD0/WAmqI98BT105STfg7Oetj+OSx7CgqXQlkhrHlF/Xgz/jpIj9xk5VBJyOq1PK+ai2hMEdXDnZP6B1dwo9dcFFfUBd0dAaqDQU896EZdl07sTbLNwvD8DOMxoCIX4JW7jlLkQp+G2js7pVFc1PkXFy1ZnutFnUergtsR6GmRUBw6oVFctBa5qGxnR8qOZNoglYdavPWw3/sPlrVvMadOvLh06gWdDlvg3eekfmrfEE2nXJ2/LdhmXPee1tpZqK534b2bj0bUVtM0jnidoCzffZR7P9hgFJq3K1Y7DD0DrvkQfrEHrnxb1Zb0HA/WZDVAc+JNKtLRgSTknlSPXNQ0uKioc0Z8PsKynWqK5eT+OUE9vmuaHbNJ1SKUVNbRLciddomnTsBqNhkH6nF9stlw32wKj1Yz40+LjMcObCIuqqJg6lNZ5+SYR+z07JIccLx9MOIiO1XdF+3IRbCulJ0lcgGq6PCxBdv4ctthnC431ibGYUYxZzvVW+ikO6wcrqiL+eFljT4XgT+LXT2fu2h3v2zcX87HnuglRNfbJ1ZpKkb9pkUisA39RO6Hk/vw0rI9vPDNbuqcbh4+f2TEtxc0jjRV4DlwVsetIQBtilw88sgjmEwm7rjjjggtJzIk2y3GGWik6y6OVtWz+WAFABP7BRe5sFrMhuBp6lHREnruu2uaSonomM0m+uSk0C29MXQ8KE+JC/3sr97lbtUoKVT2eVIimck2MpJsxnu8YFMxEx/83NjJ6Tu4jJYiF3paJMiai3BaUcE7LdLy56DCEBftU2PQkYzulUWXFBsVtU6+2VHCxv3lPhE+Q1ykt6+4iBevCyNy0ULbsp4OjKYngtut8ev31vmctZfWhDazJxHQIxV6irrO6faZWhsJ9Mhnit3CA+eN4MnLVTfK26v2NhvRICjCFhcrVqzgmWeeYdSoUZFcT8TQW+jCccVsiW89UYvBeWkhFWr17apy24Hsu/3h3SnSFJPJZIibNIfVCGF7d1NEOnrhXcwJjWOyC49WU1xRx49e+Q4IMnKREl7NRaitqHpBZ6s1F54dlO64mMhYzCZOGqxSI9e+sIIzHvuKu99ZZ6Ts9K6m7pntW/eQao8XceGpubAF/qzok3XrojiZ+fUVRXxXWEqq3cLF43sBnTty0S3dYZQtRDo1okeR9e6zM0f1YExBFvUuNy8t2xPRbSUKYYmLyspKrrjiCv75z3/SpUtgI56ORD+rL46wqlzqERdTgkyJ6IQy50KnMXLhv/pbz+sOykszCu+sFnPjMKUI76S9izkh8NyJYMRFVkpwhZY6bW1FDTYt0hlqLkClRgBDULy+ooiLn1nKK9/uMf7P7eVxoRM/kYtG++9A2D0pk2hGLp5dshOAn546mCHdVQ1WWSecCKqfGGQk2Uiz60XmkRVZeuQiJ7VRcN9wYn8AXl62J+KRkkQgrD3p3LlzOfPMM5k1axa/+93vWnxsXV0ddXWNB/jy8pZthyOFvmOMpEvn4Yo63v1uHwAnDgrCnMGLAZ4ZGzsOtywunC43895YS4+sJCMy4C9yAXDhuN7sOFzFnBHdfW5PdVipaXBFfCetRy56d1FRmKYH4t7ZSnSUB1VzoUcugi3oDK8VVU+LVNQ6W/RP0HdQnSEtAnDasO5MHbiXvjmpjC3owt3vrGPVnmM+w+/au6AzXlw66xqCT4vURVFc6CcfM4d2Y3VhKdBZIxfqb05LspKRbKOizhnx4WX6yYn3id7s4Xn0yEziQFktS3eUGF1DJZV1pCfZEtrtNxhCFhevv/463333HStWrAjq8Q8//DD33XdfyAtrK3rkIpIunX/8dDMVdU5G9crklCbtZ63RKC5aTot8v6+MD9YqO+9LxvcGAouLZLuFe88Z3uz2NIeFI5WRPwMsOtpy5EIf8653uART0Bls5KIqzILLjCQbFrMJl1vjWFUD3TP9iwuj5qKTRC5SHVZeuWGy8fvEftl8sHY/f5m/1YhmtHvkIsqdTpGi1vC5aCEtYtRcROeM1u3WfKJt+netLMg0YyJheNQ4rMYJT6SLOvU2VO/IhdVi5uSh3Xjl20IWbilm5tBurNpzjEv/sZTZw7vzhKcuo7MSkrQqKiri9ttv55VXXiEpKbgdz913301ZWZnxU1RUFNZCQ6VbRmS9LhZuKeaNlcpf4t5zhvsUWAaDPnp895GWJ3TqZyDQ2MaWG2LPf7TCy3tL9ZoL/5ELPQqhnz1ltWDApLeiltU04AwiL12qv2ZyaAZBZrPJ2JaeN/VX6Gqc/XSCbhF/9M5OYe7Mgfzf7CHGbR2VFonWXJxIoUcuklqKXFiiW9BZ3dDYfpmRZDO+a50zctF4YqB3BgbtdREkR/S0SJMU9Ywh6iRz0ZbDaJrG419so8Gl8eH3B4zJ2Z2VkPakq1atori4mLFjGxWZy+Xiyy+/5IknnqCurg6LxVfNOxwOHI72N8RpjFy0XVx8v7eUuZ5ixcsmFrQ48CkQPTKSDJOgwqPVRiSjKau9+uK3eeozugaIXATCKIyL8BmgUXORrUcufD8+5bUNuNxaUDUXmck2TCZlSlda09BicWyd02WkRTLDcIzsmmbnSGUdR6vq+WLzIW55+TvmzhjI7bMGGY/pTD4XLXHTSf2xWcykJ1lDTkG1lbhJiwQRudBTJvVRKujUawpsFhMOq7kxctEZxYX+3XVYqUtW+4nIF3Tq4sJ3P3XCgBzsFjOFR6v5ZP1BFm1p9I55edkefnXmsIiuI54IKXJxyimnsG7dOtasWWP8jB8/niuuuII1a9Y0ExYdiX7w3ri/PKgz45a494MNVNe7mDaoK/f5SUMEg9lsMiaE7mihqNM7cqETcuQiCjvpitoGI91hRC6a1Cdomtq5BdOKarU07hBba0fVX89sauxQCQW9vmN3STV3v7OOeqebT9Yf8HmM3lnTWSMXOiaTietO7MdFnpRcexIvw8uCK+iMbuSiwhDDNkymRh+c8lpnVIwDYxlvjxo9VRuttEjT4vpUh9Xo2vu/t74HGmvP3li5t1MXeoYkLtLT0xkxYoTPT2pqKjk5OYwYMSJaawyLod3TyUy2UVXvYv3+8ItIXW7NeP4D545oU5GOLni2ByjqLC6v9euDEajmIhDRSIvo6+qSYjMOAulJVgZ2S6NnVjJJHrfCY9X1QUUu1GsF1zFS5lXDEWo6ChrFxW/eW2/U4GwvrjTSI3VOl3GG2VlqLmIR/XMVrbk4kaI2hILO6IkL3zSe/l1zubWYF2eRxrteKtBIgrbir1tERy/krKxzkmQz89Tl4+jVJZmymgbe8TQAdEYStpzVbG70gdC9KcJhT0kV9U43STZzUBNQW2KgXtRZ7D8Xt7qo1O/tIadFonAG2FjM2fgemM0mPvzxiXzx8+mGSVhpdX1Q3SKAMRSrtY6R0iBfLxBNBznZLWacbo2tB5XI804fpYbooyFEjvix/w6ioDPKNRflTdJ4STaLIXZKg+zAShQqvaI46VGqN2vqc+HNFZMKuGPWIO49exif3H4SI3tlcu1UNan0H1/uCGnkQyLRZnGxaNEi/vrXv0ZgKZFH94FY1gZxsfWQOgAN7JYW1lmzN4O7q+Fi6/aV+r1fT4n08Rom5bCaQ04FRCMt0tRAS0ft1CyGUDhSWW+cSbQmBvSD/jvf7W3R/8PoPkkJb9qjd2Hi7y8Yyfi+qmZmw341gE7fEaXaLUENohOig/7Z2l5ciTuGd8j696pFE62o11w0rxHqrHUXB8rUiU96ktVoJY9kQafLrRnRVX/iIslm4Y5Zg7lmaj/6dVWp70sn9CYrxcbukmr+t/5gxNYSTyRs5AIaZ3+s2H0s7LqLbYeU1fdgz9TRtjCxrxrFvPVQpd8ulrWeyMU5o/ON23LTQ59M2Zi7jly+r6mBVlN0U6zCkmrjttbExRCP2Pps4yEueWZpwFyx0X0SZuTionG9uHpKH96+9QQumVBgDH3b4El3dZZx67HOkO7pJNnMVNQ6W/WD6Uj2ew5m+S3MXnFE2edCT4t4t4MbdRedSFwcLKtlpceb5YQBOY2RiwimRY5U1uHWVM1XlyBPcFIdVq6e0hdoNDvrbCS0uDiuRwYZSVYq65zGgUTnUHktT3yxjYWbi1my7QjPfrXTb+5/q+eMWo86tIUuqXaG9VAHtqU7fKMpmqax3nMmfdqw7tgsSlCEWm8B0QkvGwZaAVJDeuRit6f9KsVuwWZp+eM179QhPHH5GEBVYwfaEZd6evdbam1tiW4ZSdx37gjG9VERi+H5mUDzyEVnL+bsaGwWM6N7ZQH4mHnFEk6Xm/2l6sTAO0XYlPYr6OzckYv/rt2PpsGEvl3o1SUlKjUX+n5iQG5aq/s0b66YXACoiHSkx1DEAwktLixmE+P7qtTImib1DE8u3M6fPtvKtS+s4MrnvuV3H23ij59ubvYaWz1Dygbn+W8dDZWpA7sC8M12X3FReLSailondouZId3TDRfMUOaX6KQ5Im//HWzkYo8nchFMlMFiNnHGiB6tzgNoa+SiKXrkYvPBClUAZ0QuOoc7Zywz1iMA2zqq/NH5W5n1l8WGMI0UB8pqcbk17Bazz+DApninRaLRvaFHLrwnPuviu7QTiYv316qCyXOO7wkQlZqL9fvUiemInpkhPa9behKjeqnnLNp6uJVHJx4JLS6g8UCysUnkYtuhxrBrhucD+cXmYp8dQYPLzc4j6nGDIpAWARW6A/h6xxGf29ftU+p4aI907FYzBZ66i3AiF3reMZKhwUZxEShyobd7qshFS22o3pjNplbnAbS15qIp/XPTSLKZqa53sbukqtHpUCIXHY7uIfNdYSm1Da4WDeda4m8LtrG9uJI3VkbWtE//HvTsktxiDZbD05avaeCMQv2Iv8hFRieLXOw6UsX6feVYzSbOHNkDaEwTRXK2yHrPvlk/loTCTI/J1sLNxRFbT7zQacTFhgNlPrfr7mlv33oCy381i2SbhUPldWw6UOHzmAaXRordQs8s/2fsoTKhbzZWs4m9x2p86hN0caGr4+M86ZO+OaF3qOiTPctqGli15yjPfrWzWRomFMprG70rAr0PXTx23nu9xrIHS2tnG6URjlxYzCZjkNyuw1VGAWpnmIga64wpyAJUUeeoez/jmueXh3zm710MmtxC0WU4FAUobG6Kd8t6NFIj/kzfOltaZNMBdcI4slemURwejXZmXVyMDDFyAXCyp031q21HojrELhZJeHExrIf6QGw9WGmcBdU5XRzw5MD65KSQZLMwxRNRWLS1UWHqQmNQBDpFdFIdVmMH6h29aPoBvnXGAB6/bAw/nNw35G3oO5wthyq44Oml/O6jTdz075VhV+DrIig71W7UczQlq0lUIdjCJ/CyfQ6wQ2hrzYU/9LRT4dHqTje0LJbpmuYwuqXqXW6+3l7CpxsOhfQaR71SIU0/l21l79GWa490oi0uyr3aL3U6m7go9Pwv+nj9L9JbsZB/4ott3PryKg6V11Lb4GrVY6ekso79ZepYMSyMyMXInpl0TbNTWedk5e6jIT8/nkl4cdE7O5l0h5V6l9todyw6WoOmKZWrT83UR1B727d+4znbHxOG3XdLnDCgq8/ra5pm5PV0cZGRZOPs0flhWTAXZKcaO7fsVDsmk/qyHa4Mb4ibfobQUt1JlyYHfr3dMxhaC2UGM6skVPSDQ9GxamOuSGe3/o4VbpjWn+N6ZBjDAR+dvzUkYXywrLF4LtIJidZqj3QsZpPR1uzdjup2a/xv/UGW7iihuj78s+vGbpHGz2yWMbysc4kLb/8h3TW43uluNkPI5db402db+WT9QU76w0ImPPg5Ex/8nD/8b3NAJ03dQLF/19RmgxqDwWw2Gfv7QD5GiUrCiwuTycRxTVoP9ZRIQXaK0eY5Y7Daka3ac4zy2gY0TeNLTxHO9MGhjVdvDb2oc+mOI2iaxo7DlZTVNGCzmBgUgcLR3HQHX/xsOp/Pm86qX88iP1PtCPUdY6hs9hS16qkafzSNVJxyXF7Qr99ahXfjlNXInYX29hwcio7WeEUuRFzEAj+c3IdPbp/GXy4+nvQkK1sOVfDZxuCjF97ioiHCUQM9LdK7hU4RHX9GWn/4dAu3vLyKy/65jEkPLaDoaHWgp7dIhZ/PbGYnG15W5CeK5N1O3rTmbL+X+3Gd001FrROnW+OpRTv48Wur/W7DqLcIIyWio7fc67YGnYWEFxfQvKhT72jwNqsqyEmhf9dUXG6Nr7cdYXdJNftKa7BZTEzqnx3R9RzfO4tkm4UjlfVsOVTBo/O3AUp0OKyRyRH36pLCwG5pmEwmo07Cn7V4MOiRi+O6BxYX3lGFfl1TDTOZYGgtTxqNtEgvzw5p77HqTjduPV7ITLFx6QQ142R+KOLCq+0v3ILQQDQ61bZeg2Vv4nXx0fcH+PviHYAS1BW1ThZsCi3lo1NRF9jnorOIC3+RC4vZRKon2tt0f6Lv9/MyHPz6zON46fqJ/P3KsVjNJuZvPNTMyflYVT3/XrobgLGeVHY4DMlT4mKLVxNBNPnlu+v482dbOrz9tVOIC91bQu9XNnJ1Ob4HwOleqZGvtqmoxfg+2aRE2BLabjUzweMe+tiCbXy07gBmE9w1Z2hEt6PT07Mj3BdG5ELTtEZxEWTkQjcvC5bGtEhzceFya0Z+OVz7b3/oZ55FnhZgkMhFLKJX2y/eejjo1IhP5CKC4qLO6eKQx/yutZoL8PW6qHe6+e0H6wG4+aT+3HxSf0AZ/IWD/pnN8O4WiUKnRKzidLmN/VlBk6L3QLOV9E62EfmZ3DCtP9MG5XL6iB5cOlEJ2Ac/3sTzX+/isw0H2V5cyc/fXMuh8jr656Zy6YSCsNc62CMudhyubPMQzdaoqnPynxVFPP7FdlwdPMCuU+xN9ZqJlXuOsftIlfEh69PkQzljSDee/3o3i7ce5oinPmHa4K5RWdPUATl8ufUwH69T1rCXTOjN0BYiA20hP0s5Ce4r9R+CLa6oJTvFjtWPQcyh8jqOVTdgMbecsknxqg2ZOjA0cZFh7Aya7xS93QYjKS70M8+qepcRXpWai9hjXN8upNgtHKmsY9PBcsMArSW8Ixf1rsjtYPeX1qJpqgMlJ7X1FJ2RFnG5+WLzIY5U1tMt3cGds4cYJmErdh9F07SQXHg1TfOZiqoTjYGFscqBslqcHr+RvHRfp9T0JBuHyuuaWYDvMfb7vieVPzl5EG+t2sv3e8v4fq9vV6HVbOJvl4wJq/ZNp1eXZJJtFmoaXOw5Wm0MsIwGa/eW4nJr9MxKpkdmZDocw6VTRC4GdktjxpBcXG6Nx7/YbnQ/9Gly9jGpXzZJNjMHy2tZ4OlL1msxIs2cET1ItllItVs4a1QPfnF6dKIWAD2z1N/pL3Lx2YaDTHpoAX+Zv9Xvc/WoRf+uqS3OUjCZTPz6zOP44eQ+nD68e0jrayktood40xzWkNzxWiPJZjFMkLZ5Cn39TTwUOhaH1WJ4wywO0ojoUJTSIvqMIu9arZZweEUu/rNC+W1cMK4XVouZ0b2zsFlMFFfUGZHUYKlpcBnDsLwFsTFuPManykYC/YSgV3Zzv5FA+5Pdnv1+366++/1uGUncf45y8J09PI/e2cmYTTC5fzb/vGo8I3uFX28BqqhTPzGLdt3Fdx7RqpvRdSSd5lTtjlmDWbTlMO+u3mvsGPo0qQtIslmY0j+HhZ6OkSsmFYTVfhQMBTkprPntqZhNpogeNP1hpEX81Fw8uXA7mqZy2v/nR+BsDCIlonPDtP5hra+lgs62TkRtid7ZKRRX1OFya6Q7rEzo1/FfSKE50wfn8vmmYhZvOcyPZgxs9fEHIljQWXS0mmtfWMHk/tnGAKoLx/UK6rl6WqTwaLUhjC4er0LwSTYLo3plsWrPMVbsPtbsbLol9O+JxWzyiRjqB1W9UyJS9VuxiL96C51A80UCRS4ALp7Qm4s99T2apuF0axHdLw/qls73e8vYcrCS00dE7GWboUfExrWhRiRSdIrIBagiytnD83BrKo+fZDPTPaP54KE5I5TT27g+Xfjt2cOjuiaHtfX5G5HAKOg8VuNjSLS68BhrPWHA7YcrjVztgbIao1MmmHqLtmLUXPgJ50ajmFOnt1dR3qnD8xJ6ZxzP6N1Vq4tKg6q7OOQtLtrojvnB2v1sL67k5WWFHKmsp39uKlef0Deo5+ri4n/rD+LW1D7Fu9BZb9desSs0/wPvGiHvCIp3zVBVBIcWxiLBiAvv2hO3WzMKOlszJjRF4YRvSHcVudhaHL3Ihdut8Z1nsva4PpFtQgiHThO5AHjssjG8t3ofG/aXM7Fftt/x2heO60XPLsmMLejiY4QTz+jioqreRXmN02hZe+Gb3cZjNA3W7S1jyoAcrnthJZsOlPPS9RNZ7tnxjW5jaLAl0vzsDECFtz/8/gAQHXHhvWM6a1SPiL++EBl6Z6dgNqkz8iNVdXRLDzyNtLLO6SNS25oW+dbz+Teb1EHn/nNGBL1f0Gsu9DRN/yaR0ol9s3lm8U5W7AlVXPj3ZdE7JarqXVTUNhiulYlIi+JCH3/g9Tk4VFFLndON1WyKmNtyKAzyFHVuPlAeco1NsOiWBsk2C0N7RGZcRVvoVOLCYbVwSStVv2azyThTShSS7aoAraSqnr2l1WSmZFJaXc8nnmLSQd3S2FZcyZq9pditZiNa8fiC7RRX1OGwmqOaw/OXFqltcHH+U98YqZxQWluDRW9HzUiycuLAyHqZCJHDZjGTl5HEgbJa9h2raSYuNE1j7d4yBuel+XSKQNvSIk6X28hhv3HzFPIykoLqEtHRRYjuAtm01Vmf0rvzcBVHKuuCHlLor5hTJy3J6hEXiVt34d3B5u//4c/xd/eRRtt2f4Xr0WZkz0ysZhM7Dlfx6YaDnD4i8iczekpkdO/MdomIt0bHr0BoF5q2o3687iD1LjfH9cgw8sBri0p5dXmh8ZzlHrvaif2yWyzmbCt6C513jvQ/K4rYV1pDXoaDP144il+fOSzi2z1laDdG98pk3qmDEyZKlajoZ5v6uHNvnlq0g/Oe/Jq/fLbVp5gT2ha52HSggso6JxlJVsYUdAlJWECjuDjmSe01bXXOSrEbHggrg2xJvevt77nqX8sB/4P29G0kcsfIlkMV7Dhchd1i9tv27s8CvKV6i/aga5qDW6YPAOCe9zc062SJBOs9Vguje2dF/LXDQfaonYSmLp3vrVajis87Pt/4MK7YfYyPPGkI75TRiVGO5DRWd6svXL3TbZgN3XbyIC4a3zsq4iYnzcH7t53INVP7Rfy1hciSb4iL5kXJf/x0CwDPLtnF4Qpfi/u2tKJ+u0t1h4zv6z+F2hp6WqS6XtU/+JvLY9RdBDF3QtM0Xl/ROOXVX3tkS54xicIHa/YDamSDv0Jvf++B7ieiu2V2BLedPJB+XVMprqjj0QDdeW1Bn/Q9OEITvNuKiItOgv6lev6bXWw+WM7y3UcxmeCc4/MZ0TMDi9nE0ap66pxun7kOQNTTRPqZRlW9arH78Pv9HCirpVu6g4uCrMwXEpv8AC6z3m6UvbokNztjb0vkQq83mtgvvOK4ptEwf+JCf+1ghlo1LXg+yc9YgvQWPGPimQaXm7mvfMcJDy8w2nrPHp3v97F6RKfSc7Li9PiMQOOU0o4gyWbh/nNVk8BLS/ew83BkHTt3eF5Pn/jc0Yi46CRcP60fvbokU3S0hrMeWwLA5H459MhMJsVuZdZx3bCaTcwensdTV4xllmc2SHaq3XA4jRY+8wDqnHy1TU2LvThKEQsh/gjUTu3tfZGXkURNvW+XRLji4kBZjfE5nBQhcZHmaP5ZHt9Xvfb6/eVUtZLK0AeSOaxmdjx0Btef2DziZqRFEihyoWkav3lvPR+tO8D+slpKqupJtlk45Tj/QqFpDdd3haUcq24gK8XG+A72f5g2KJcZQ3JxujUe+nizT/deWyitrudIpUq/DYgRcdGpCjo7MxlJNv526RgufmYpTrdGfmYSd5/R6Gvx9yvHUe9yG+2Y3TOSWLH7KFMHdo3YuPlAOKwW7FYz9U43FbUNfFeoQpgTwtypC4lHT91ltokRnPfMkep6F1X1kYlcPPLJZmoaXIzv04Xjw8xhO5pGLvyMEeiZlUzPrGT2ldawpqi0xSih93TgQGka/cCaSEZary4v5PUVRZhNcOnEAj7bcJDLJxYEHMvQtKBz/kZVuH7ykG4dUszZlF+dcRxfbTvC55sO8a+vd/sViaGiT/zOz0yKmTEGsbEKoV0Y16cLb9w8mbKaBk4alOvzRTOZTD4+D8l2C3+8aHS7rS0jycqRynr2lFSzp6Qak4mwd+pC4qG7zO4v8xUXX3pFLmrqnUbkIt1hpaLOSb0z9DPDNUWlvL9mPyYT3HvO8LDbBu2WppEL/7vbQXlp7CutaXWwYFkQhnJpftow28rCLcW8ubKIM0fmc/qI7mHVn4TL3mPVPPTRJgB+cfpQbp4+gId+MLLF5+g1F1sOVXDek1+zxTPVedaw4Cc1R5NBeen88ozjeODDjTz40Ub65qSENEXaH7q4iJWoBUhapNMxrk82Jw/NiwkF742+Q9DD3IO6pUXFlVOIT/T5OKXVDUb6oLre6VNzUV3vMoonMzyfnXAiF8s9hZyzjstjRBtGbQdTcwGNc3mapnSaUupJi2QlB/avSAvgThkudU4X//fW93y87iBzX/2Om/69MiKvGyy/fX8DVfUqgnRjkA7A3iJuTVEpNQ0uuqTY/NaodBTXTe3LReN64dbg1pe/C3s6ro4uLmKl3gJEXAgxgr5DWLRFzXQZFwPe+ELskJ5kM0L+esdIiSfHrFPjJS4y2yAu9ANzj8zAZl3BEKy4SLap22saWhEXNervzWzBUC4jgCFduLy3eh+HK+qMIsnFWw9HfbKnjtut8aVnOvV95w4POj3rbR520bhevHnLFD6fNz1m0gWgIsUPnT+SOSO6U+9yc8vLq3xSfC1R73Sz+WA51V4pwG0xKC5i590WOjX6gWOrp51qbIGIC8GXnlnJbD5Ywb7SGgblpRuTi61mE063RnWDy9jhtkVclHvZa7cFu8W3gDPQ6yXbfVtWAxFcWiQyPhfvrt7LGyv2suuI8oe47eSB/GX+VuqcbvaV1rSLX8Sx6noaPK3Eg0Jor8xOtfPAucOxWsxcOqF3VNwwI4HNYuaxy8Zwx+tr+GjdAW59eRUXje/FhL7ZjO+T3WyUvM5db3/PO6v3YTbBBWN78fsLRjVGLqI4cTVURFwIMUFTK+NYmOonxBbe4gIaIxe9s1PYdaQKl1trdgAOx+dCPzA3ddQMlWbdIgFeTy9MrKkPrlskqyVx0cIQwFB4cuEO44CVnmTl8kkFvP3dXrYeqmTXkap2ERfFHs+S7FR7yCZ3P5zSNworijw2i5m/XXo8ZrOJ/67dz2vLi3htuWq1/dGMAc2GSa7bW8Y7Ho8itwZvrtqLw2Y2vhOxFLmQtIgQEzi9DgInDMhpNodBEPrnqs/EF5tU6qykSh18enkNoNMFhxG5CMP+u7IFe+1QaHpATAnQVp3sub21yIVRc9FCWiQSJlqaphmpp/OOz+exS8eQnmSjr0dQ7PZEM6KNLi66pQdnix6vWC1m/nbJ8Tx39XhuOqm/Ucj+r693GYMbdf7w6WYAfjCmJ/ecpVyLX16mXJVPGdqNnCAt5NsDERdCTOBd5fzk5WNjNpQpdByXT+qD2QQLNhezbm8ZJZ6ZHXkZSdgs6vOiCw69LsHpDl1cVHgMqPzZa4eCt7hItVsC1gwEW9DZXmmR8hqnIXQeuWAUMz3GU/p8n92e6aLRpthj5Z6b4OIC1EyrU47L45dnHMe7PzqB43pkUNvg9nFk/XZnCV9tO4LNYmLeqYO55oS+TO6v2vUn9cvmicvHdtTy/SLiQogJrprSh5un9+er/5tJlwSe5iiET7+uqZx3fE8A/rZgqxGlyEm1G2f/+pCwxpqLMNIiRuSibeLC4dWRFaiYExrFRauRC6OgM/D3o9GhM3xxoYfYc1LtPiZ2fT3iYle7Ry7aVlgbb5hMJq6d2heAf3+z2yigfebLnQBcOK63mhRsNvHc1RP4+5XjePG6iX7t4DsSERdCTNCrSwp3zzku5OFQQufitpMHAvD5pmJjGFVOmt2oW3B7tIRRcxFGWqQiUgWdXpGLll4r2R5kt0gQNRfpEWhF1VMi+U1GkxtpkZJ2EheeyEW3jMSPXDTlnNH55KTa2V9Wy2NfbGfroQq+2FyMyQQ3ndTYkpvqsHL6iO4x6WQs4kIQhLihf26aMZpcHzGdk+owzv512tItUhGFgs6WIhd61KW1tEh5CGmRepeb2lbECsB3hceMuRs6ulGZ7i2io6dF9pRUM+0PX3Dek18b29A0jQNlNRGzs4bGyEVeJ0iLNCXJZuE3npqKxxZs47oXVgAwe1h34/8Q64i4EAQhrtCLfY95zuRz0uzNQsKR8LnIaGtBp09aJPCZpZEWaWg52lBa03pBp7fFeGupkf2lNVz2j2Vc98JKthdXeN2uIgZNIxd5GQ5DCBUdVXblT3yxnQWbDnHek18z5eEv+P3/trS4zVAw0iIZnSstonPemJ78cHIfQE2zTk+y8pNTBnXwqoJHWlEFQYgr+nVNZbnXFFF/kQv9ABxqzUWDy22kJ9ovLdJ6zUW9093MIMwfZrOJNIeVyjonlbVOuqY5cLrczHtjLUer6pk9PI9zx/QkI8nGnz7bQp0nbfTZxkMM9HhJ6GmRnk3Ehclkor6JWHti4Xaf3//x5Q7OHt2D4fnhO5vqFFd40iKdMHKh85uzhtE/N5WsFBszBneLq3o0iVwIghBX9Mv1DQuryIXvwbvR5yK0yIX3ZNL2SosE0y2id4qYTK23yDadCvrNjhI+WLufJduP8Jv3N3DCw19ww4sredfjlwCwwNPeC43iokemr7gA1SYO0DcnhdOHdzfWf8v0AZw6LA+3Br9+b31YtS7eaJpGcXnnLOj0xm41c+3UfvxgTK+4EhYgkQtBEOKMvk0MnLJT7T4eElazySjwbHC50TQt6NZm/YCcZDNja+P8nVDFRUuRizJPp0hGUuCJqDp6lERvqf143QEAxhRkUVnrZFtxJZ97ZlmcMCCHb3aU8F3hMUoq68hJc3gVdDY/qP/mrGG8saKIW2YMINVu5ezNxUwZkEN2qp2DZbV8s/0IqwtLueHfK/n7lWNJsVupbXDhsJqN/4HbrbH9cCV7j1VzXI8MvyKmvNZpRFU6Y0FnIiDiQhCEuKK/V+Qi3WElyWbxSYsk2y1GvYOmgcutYbWEJi706aJtwbvmoiXPjGC6RfROkWCG+XkPL2twufl0gxo5/vPThjClfw5Lth9hz9Fq0h1WTh2Wx8XPLGXD/nJeX1HEVVP6cNDTpdE0LQIwOC+dX3sKDQHOHNXDuN49M4mnrxzHzS+t4suth7n/vxu5cnIfLvz7N/TvmsZ95w7HajZx7383sraoFFARkIU/n9FM/OmdIhlJ1pjshBBaR8SFIAhxRUF2CiaTEg7ZaSpU7F3QmWK3+IiJBpeGNcjjk14EmdHGlAio6IdOMN0i9U43LrfmNzJRFkQxp06up5tmwaZiku0WjlU3kJ1qZ1K/bMxmU7PpoKccl8eG/eX88dMt/PFTVZBps5iMrpxQOGlwLs/8cBxX/Ws576/ZT2l1A7UNbjYeKOeivy81HpdkM9Pg0thdUs2Ow5VGvYdOZy/mTASk5kIQhLgiyWYxzqpzPHlo78hFqt3qk9IIpe5Cnyba1noL8B1cFkxaBPCZdOlNKJEL3QfhPyuL+OW76wCYPbw71gBpnqum9OGc0fk+TphujaCnkDZl2qCu9O+aSk2Di/95oiZTB+aQYreQbLMwe3gei34+kyn9Vf3Gkm1Hmr2GFHPGPyIuBEGIO/Ref32WgndBZ7LdYtiBQ2jtqMbQsgiM5/btFgkcOlH1COp6oKLO0iA8LnTG983mikkFgGoZzUm1c/2J/QI+vmuag8cuG8NX/zeT0Z65FtMGdW11O4EwmUycP7an8Xv/3FRevn4SG+8/nU0PnM4zPxxP98wkpg5U21iyvcTn+VsPVfD817sBZe0uxCchiYunn36aUaNGkZGRQUZGBlOmTOGTTz6J1toEQRD8YogLP5GLFLsFk8lkCIxQxEVFhKy/IfiCTpPJZBSkBirq/Hq7OrsPds7GL+YMZeaQXC4c14vPfnpSUNMyk2wW3rx5Cn++aDQPnDsiqO0E4gdjexmC6eLx/see6wJm2c4S439UdLSaC576hu/3lpFqt3Dl5II2rUPoOEL6BvXq1YtHHnmEQYMGoWkaL774Iueeey6rV69m+PDh0VqjIAiCD+eN6cmqPcc45/h8gCYFnWq3ZrOYaXC5aHAG73UR0YLOIMUFqDVX1bv8iosFmw7xxeZirGYTV0zqE9S2M5JsPH/txNAWjFrzBeN6hfy8pvTMSuaKSQUs33WUiwK83rAeGWSl2CitbmBtUSnH987ip/9ZQ0Wdk9G9Mnn6ynHNjLyE+CEkcXH22Wf7/P7ggw/y9NNPs2zZMhEXgiC0G2MLuvDRT6YZvyd7dRToUQBVd+GiIYTJqJX6RNSI1FwEZ6IFXl4XTTpGNE3jdx9tAuD6E/sFFYGIFX533sgW7zebTZw4sCsffn+ABz7axOBuaazcc4w0h5UnLh8rwiLOCbvmwuVy8frrr1NVVcWUKVMiuSZBEISQSPGquUhxeIuLEGsuIpgW8a77SLW3ErkIMF+kpKremEKqD21LJOadOpisFBtri0p5c9VezCZ46PyRMsAwAQj5G7Ru3TqmTJlCbW0taWlpvPvuuwwbNizg4+vq6qirqzN+Ly8vD2+lgiAIAWhacwFg12suwkqLtF1cmEwmUu0WqupdZLbSQtpoAe7bLXLY05KZk2pv1ZkzHumfm8ZzV0/gyme/xWo28djlY5g5pFtHL0uIACF/g4YMGcKaNWsoKyvjrbfe4uqrr2bx4sUBBcbDDz/Mfffd1+aFCoIgBMLX58JTc+GpeQipFbVOj1xE5kB+z9nDOFhW59eQyptAaRHd7yHYQs54ZFyfLnz5fzOxW81BdcMI8UHI4sJutzNwoArPjRs3jhUrVvC3v/2NZ555xu/j7777bubNm2f8Xl5eTu/evcNcriAIQnN8CjptbU+LRMLnAuCSCcF1OwSyANedKhNZXEDi/32dkTZ/g9xut0/aoykOhwOHQz44giBEDx8TrTbUXOjzOFqy644GeodLU3FxuFKGdwnxSUjfoLvvvps5c+ZQUFBARUUFr776KosWLeLTTz+N1voEQRBaxddES123h+FzEcmCzlDQO1xqm6ZFyhM/LSIkJiF9g4qLi7nqqqs4cOAAmZmZjBo1ik8//ZRTTz01WusTBEFolZSArahQH05BZzuLi4AFnUbkQsSFEF+E9A167rnnorUOQRCEsGk6uAxCT4u43RrltbrPRfsWFiYHqLk4XK4P8BJxIcQXMltEEIS4x2E1o8/Z0g/U1hDTIgfKa9UEVbOJvHaOFKQE8LnQB3jlhjGhVBA6EhEXgiDEPSaTyWhB1a227SFGLnZ7zKoKslMCThCNFgEjFzJ6XIhTRFwIgpAQZHmMqrI8XglGzYUruJoL3Qmzr2coWnuS4qdbpKrOSZXndynoFOKN9q1aEgRBiBIP/mAkWw9WGPM3dBOtBmdokYt+HSIumneL6FGLFLslIo6hgtCeyCdWEISEYPrgXKYPzjV+D3XkekdGLpJsvt0iy3aWsOmAGpUgnSJCPCLiQhCEhESvuXC6g0yLlHgiFzkdF7mornfx+cZD3PDvlcZ9khIR4hGpuRAEISFp9LloPXLhdLkpOloNQN+u7T+RUxcXB8tr+dmba33uE3EhxCMiLgRBSEhC8bnYX6raUO1WM/mZLQ8ZiwZ5nm6Q0uoGymoaGNo93bjPFWTkRRBiCUmLCIKQkNiswddc7DxSCUCf7BTMumFGO9I7O4V/XTOepTtKKK1uYN5pg5m/8RC/+2gTF4+XQY9C/CHiQhCEhKTR56L1M//dHVjMqXPy0DxOHppn/H7VlL5cPrGg3T03BCESyKdWEISEpNHnovXIxZZDFQAMyE2L6ppCRYSFEK/IJ1cQhITEqLkIoqBzTVEZAMf3zozqmgShsyDiQhCEhCRYn4vqeidbPZGL0b2zor0sQegUiLgQBCEhsQVZc7Fhfzkut0a3dAfdZYaHIEQEEReCICQkwdZcrC0qBVTUwmRq/04RQUhERFwIgpCQBJsWWeMRF8dLSkQQIoaIC0EQEhK7tXUTLU3TRFwIQhQQcSEIQkLS2C3iv+bC6XLzy3fXsfdYDTaLiRE9pVNEECKFiAtBEBKS1mounv96N68tL8JkgvvOGUFmsq09lycICY2IC0EQEpJkzxjzwxV1aFrz6IWeDrn9lEFcPqmgPZcmCAmPiAtBEBKSMQVZJNss7CutYbVHSHhzsLwWgMF56c3uEwShbYi4EAQhIUl1WJkzojsA73y3t9n9B8uUuMgTbwtBiDgiLgRBSFjOH9sLgA+/P0Cd02Xc7nZrFFcocdE9U8SFIEQaEReCICQsUwbkkJfhoLS6gbdX7TNuP1ZdT4NLw2SCbumODlyhICQmIi4EQUhYLGYTN07rD8BDH29i77FqoLHeIifVYXSVCIIQOeRbJQhCQnPt1H6M79OFyjon97y/AYBD5XpKRKIWghANRFwIgpDQWMwmfn/hKAAWbimmpLKOg2V1AOSlS72FIEQDEReCICQ8A3LTGJ6fgabBF5uLjbRInhRzCkJUEHEhCEKnYNZxeQAs2FTMIU8bqoxYF4ToIOJCEIROwanDlLj4ctthCo+qwk4RF4IQHURcCILQKRien0H3jCSq610s3VkCSFpEEKKFiAtBEDoFJpOJs0b18LlNIheCEB1EXAiC0Gn48SmDfKafirgQhOgg4kIQhE5DZrKN+88dDkCaw0pGsrWDVyQIiYl8swRB6FSce3xPHFYLXdPsmEymjl6OICQkIi4EQeh0nO6ZlioIQnSQtIggCIIgCBFFxIUgCIIgCBFFxIUgCIIgCBFFxIUgCIIgCBElJHHx8MMPM2HCBNLT0+nWrRvnnXceW7ZsidbaBEEQBEGIQ0ISF4sXL2bu3LksW7aM+fPn09DQwGmnnUZVVVW01icIgiAIQpxh0jRNC/fJhw8fplu3bixevJiTTjopqOeUl5eTmZlJWVkZGRkZ4W5aEARBEIR2JJTjd5t8LsrKygDIzs4O+Ji6ujrq6up8FicIgiAIQuISdkGn2+3mjjvuYOrUqYwYMSLg4x5++GEyMzONn969e4e7SUEQBEEQ4oCw0yK33norn3zyCUuWLKFXr14BH+cvctG7d29JiwiCIAhCHBH1tMhtt93Ghx9+yJdfftmisABwOBw4HI5wNiMIgiAIQhwSkrjQNI0f//jHvPvuuyxatIh+/fpFa12CIAiCIMQpIYmLuXPn8uqrr/L++++Tnp7OwYMHAcjMzCQ5OTkqCxQEQRAEIb4IqeYi0Hji559/nmuuuSao1ygrKyMrK4uioiKpuRAEQRCEOEGvmSwtLSUzM7PFx4acFmkrFRUVANI1IgiCIAhxSEVFRaviok0mWuHgdrvZv38/6enpASMh4aArKomIBI+8Z6Ej71noyHsWOvKehY68Z6ET6numaRoVFRXk5+djNrfsZNEmE61wMJvNrXaYtIWMjAz5YIWIvGehI+9Z6Mh7FjrynoWOvGehE8p71lrEQkemogqCIAiCEFFEXAiCIAiCEFESRlw4HA5++9vfimFXCMh7FjrynoWOvGehI+9Z6Mh7FjrRfM/avaBTEARBEITEJmEiF4IgCIIgxAYiLgRBEARBiCgiLgRBEARBiCgiLgRBEARBiCgJIy6efPJJ+vbtS1JSEpMmTWL58uUdvaSY4N5778VkMvn8DB061Li/traWuXPnkpOTQ1paGhdccAGHDh3qwBW3P19++SVnn302+fn5mEwm3nvvPZ/7NU3jnnvuoUePHiQnJzNr1iy2bdvm85ijR49yxRVXkJGRQVZWFtdffz2VlZXt+Fe0L629Z9dcc02zz93pp5/u85jO9p49/PDDTJgwgfT0dLp168Z5553Hli1bfB4TzPexsLCQM888k5SUFLp168add96J0+lszz+l3QjmPZsxY0azz9ott9zi85jO9J49/fTTjBo1yjDGmjJlCp988olxf3t9xhJCXPznP/9h3rx5/Pa3v+W7775j9OjRzJ49m+Li4o5eWkwwfPhwDhw4YPwsWbLEuO+nP/0p//3vf3nzzTdZvHgx+/fv5/zzz+/A1bY/VVVVjB49mieffNLv/X/4wx947LHH+Pvf/863335Lamoqs2fPpra21njMFVdcwYYNG5g/fz4ffvghX375JTfddFN7/QntTmvvGcDpp5/u87l77bXXfO7vbO/Z4sWLmTt3LsuWLWP+/Pk0NDRw2mmnUVVVZTymte+jy+XizDPPpL6+nm+++YYXX3yRF154gXvuuacj/qSoE8x7BnDjjTf6fNb+8Ic/GPd1tvesV69ePPLII6xatYqVK1dy8sknc+6557JhwwagHT9jWgIwceJEbe7cucbvLpdLy8/P1x5++OEOXFVs8Nvf/lYbPXq03/tKS0s1m82mvfnmm8ZtmzZt0gBt6dKl7bTC2ALQ3n33XeN3t9utde/eXfvjH/9o3FZaWqo5HA7ttdde0zRN0zZu3KgB2ooVK4zHfPLJJ5rJZNL27dvXbmvvKJq+Z5qmaVdffbV27rnnBnxOZ3/PNE3TiouLNUBbvHixpmnBfR8//vhjzWw2awcPHjQe8/TTT2sZGRlaXV1d+/4BHUDT90zTNG369Ona7bffHvA5nf090zRN69Kli/bss8+262cs7iMX9fX1rFq1ilmzZhm3mc1mZs2axdKlSztwZbHDtm3byM/Pp3///lxxxRUUFhYCsGrVKhoaGnzeu6FDh1JQUCDvnYddu3Zx8OBBn/coMzOTSZMmGe/R0qVLycrKYvz48cZjZs2ahdls5ttvv233NccKixYtolu3bgwZMoRbb72VkpIS4z55z6CsrAyA7OxsILjv49KlSxk5ciR5eXnGY2bPnk15eblxZprINH3PdF555RW6du3KiBEjuPvuu6murjbu68zvmcvl4vXXX6eqqoopU6a062es3QeXRZojR47gcrl83giAvLw8Nm/e3EGrih0mTZrECy+8wJAhQzhw4AD33Xcf06ZNY/369Rw8eBC73U5WVpbPc/Ly8jh48GDHLDjG0N8Hf58v/b6DBw/SrVs3n/utVivZ2dmd9n08/fTTOf/88+nXrx87duzgl7/8JXPmzGHp0qVYLJZO/5653W7uuOMOpk6dyogRIwCC+j4ePHjQ72dRvy+R8feeAVx++eX06dOH/Px8vv/+e37xi1+wZcsW3nnnHaBzvmfr1q1jypQp1NbWkpaWxrvvvsuwYcNYs2ZNu33G4l5cCC0zZ84c4/qoUaOYNGkSffr04Y033iA5ObkDVyYkMpdeeqlxfeTIkYwaNYoBAwawaNEiTjnllA5cWWwwd+5c1q9f71P/JLRMoPfMu05n5MiR9OjRg1NOOYUdO3YwYMCA9l5mTDBkyBDWrFlDWVkZb731FldffTWLFy9u1zXEfVqka9euWCyWZtWuhw4donv37h20qtglKyuLwYMHs337drp37059fT2lpaU+j5H3rhH9fWjp89W9e/dmxcNOp5OjR4/K++ihf//+dO3ale3btwOd+z277bbb+PDDD1m4cCG9evUybg/m+9i9e3e/n0X9vkQl0Hvmj0mTJgH4fNY623tmt9sZOHAg48aN4+GHH2b06NH87W9/a9fPWNyLC7vdzrhx41iwYIFxm9vtZsGCBUyZMqUDVxabVFZWsmPHDnr06MG4ceOw2Ww+792WLVsoLCyU985Dv3796N69u897VF5ezrfffmu8R1OmTKG0tJRVq1YZj/niiy9wu93Gjq6zs3fvXkpKSujRowfQOd8zTdO47bbbePfdd/niiy/o16+fz/3BfB+nTJnCunXrfITZ/PnzycjIYNiwYe3zh7Qjrb1n/lizZg2Az2etM71n/nC73dTV1bXvZyxS1agdyeuvv645HA7thRde0DZu3KjddNNNWlZWlk+1a2flZz/7mbZo0SJt165d2tdff63NmjVL69q1q1ZcXKxpmqbdcsstWkFBgfbFF19oK1eu1KZMmaJNmTKlg1fdvlRUVGirV6/WVq9erQHaX/7yF2316tXanj17NE3TtEceeUTLysrS3n//fe3777/Xzj33XK1fv35aTU2N8Rqnn366NmbMGO3bb7/VlixZog0aNEi77LLLOupPijotvWcVFRXaz3/+c23p0qXarl27tM8//1wbO3asNmjQIK22ttZ4jc72nt16661aZmamtmjRIu3AgQPGT3V1tfGY1r6PTqdTGzFihHbaaadpa9as0f73v/9pubm52t13390Rf1LUae092759u3b//fdrK1eu1Hbt2qW9//77Wv/+/bWTTjrJeI3O9p7ddddd2uLFi7Vdu3Zp33//vXbXXXdpJpNJ++yzzzRNa7/PWEKIC03TtMcff1wrKCjQ7Ha7NnHiRG3ZsmUdvaSY4JJLLtF69Oih2e12rWfPntoll1yibd++3bi/pqZG+9GPfqR16dJFS0lJ0X7wgx9oBw4c6MAVtz8LFy7UgGY/V199taZpqh31N7/5jZaXl6c5HA7tlFNO0bZs2eLzGiUlJdpll12mpaWlaRkZGdq1116rVVRUdMBf0z609J5VV1drp512mpabm6vZbDatT58+2o033thM7He298zf+wVozz//vPGYYL6Pu3fv1ubMmaMlJydrXbt21X72s59pDQ0N7fzXtA+tvWeFhYXaSSedpGVnZ2sOh0MbOHCgduedd2plZWU+r9OZ3rPrrrtO69Onj2a327Xc3FztlFNOMYSFprXfZ0xGrguCIAiCEFHivuZCEARBEITYQsSFIAiCIAgRRcSFIAiCIAgRRcSFIAiCIAgRRcSFIAiCIAgRRcSFIAiCIAgRRcSFIAiCIAgRRcSFIAiCIAgRRcSFIAiCIAgRRcSFIAiCIAgRRcSFIAiCIAgRRcSFIAiCIAgR5f8BGMvAqGdFfEgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mfd['Interest_1_W'],label='bank_1W')\n",
    "plt.plot(mfd['mfd_7daily_yield'],label='bao_1W')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2013aeee810>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mfd['bank_pct']=mfd['Interest_1_W'].pct_change()\n",
    "mfd['bao_pct']=mfd['mfd_7daily_yield'].pct_change()\n",
    "\n",
    "plt.plot(mfd['bank_pct'],label='bank_pct')\n",
    "plt.plot(mfd['bao_pct'],label='bao_pct')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mfd_date</th>\n",
       "      <th>Interest_1_W</th>\n",
       "      <th>mfd_7daily_yield</th>\n",
       "      <th>bank_pct</th>\n",
       "      <th>bao_pct</th>\n",
       "      <th>purchase_amt</th>\n",
       "      <th>redeem_amt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20130701</td>\n",
       "      <td>5.423</td>\n",
       "      <td>6.307</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32488348</td>\n",
       "      <td>5525022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20130702</td>\n",
       "      <td>4.750</td>\n",
       "      <td>6.174</td>\n",
       "      <td>-0.124101</td>\n",
       "      <td>-0.021088</td>\n",
       "      <td>29037390</td>\n",
       "      <td>2554548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20130703</td>\n",
       "      <td>4.242</td>\n",
       "      <td>6.034</td>\n",
       "      <td>-0.106947</td>\n",
       "      <td>-0.022676</td>\n",
       "      <td>27270770</td>\n",
       "      <td>5953867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20130704</td>\n",
       "      <td>3.938</td>\n",
       "      <td>5.903</td>\n",
       "      <td>-0.071664</td>\n",
       "      <td>-0.021710</td>\n",
       "      <td>18321185</td>\n",
       "      <td>6410729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20130705</td>\n",
       "      <td>3.816</td>\n",
       "      <td>5.739</td>\n",
       "      <td>-0.030980</td>\n",
       "      <td>-0.027782</td>\n",
       "      <td>11648749</td>\n",
       "      <td>2763587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>20140825</td>\n",
       "      <td>3.363</td>\n",
       "      <td>4.139</td>\n",
       "      <td>-0.001781</td>\n",
       "      <td>-0.003371</td>\n",
       "      <td>309574223</td>\n",
       "      <td>312413411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>20140826</td>\n",
       "      <td>3.318</td>\n",
       "      <td>4.131</td>\n",
       "      <td>-0.013381</td>\n",
       "      <td>-0.001933</td>\n",
       "      <td>306945089</td>\n",
       "      <td>285478563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>20140827</td>\n",
       "      <td>3.349</td>\n",
       "      <td>4.123</td>\n",
       "      <td>0.009343</td>\n",
       "      <td>-0.001937</td>\n",
       "      <td>302194801</td>\n",
       "      <td>468164147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>20140828</td>\n",
       "      <td>3.538</td>\n",
       "      <td>4.116</td>\n",
       "      <td>0.056435</td>\n",
       "      <td>-0.001698</td>\n",
       "      <td>245082751</td>\n",
       "      <td>297893861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>20140829</td>\n",
       "      <td>3.672</td>\n",
       "      <td>4.123</td>\n",
       "      <td>0.037875</td>\n",
       "      <td>0.001701</td>\n",
       "      <td>267554713</td>\n",
       "      <td>273756380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mfd_date  Interest_1_W  mfd_7daily_yield  bank_pct   bao_pct  \\\n",
       "0    20130701         5.423             6.307       NaN       NaN   \n",
       "1    20130702         4.750             6.174 -0.124101 -0.021088   \n",
       "2    20130703         4.242             6.034 -0.106947 -0.022676   \n",
       "3    20130704         3.938             5.903 -0.071664 -0.021710   \n",
       "4    20130705         3.816             5.739 -0.030980 -0.027782   \n",
       "..        ...           ...               ...       ...       ...   \n",
       "289  20140825         3.363             4.139 -0.001781 -0.003371   \n",
       "290  20140826         3.318             4.131 -0.013381 -0.001933   \n",
       "291  20140827         3.349             4.123  0.009343 -0.001937   \n",
       "292  20140828         3.538             4.116  0.056435 -0.001698   \n",
       "293  20140829         3.672             4.123  0.037875  0.001701   \n",
       "\n",
       "     purchase_amt  redeem_amt  \n",
       "0        32488348     5525022  \n",
       "1        29037390     2554548  \n",
       "2        27270770     5953867  \n",
       "3        18321185     6410729  \n",
       "4        11648749     2763587  \n",
       "..            ...         ...  \n",
       "289     309574223   312413411  \n",
       "290     306945089   285478563  \n",
       "291     302194801   468164147  \n",
       "292     245082751   297893861  \n",
       "293     267554713   273756380  \n",
       "\n",
       "[294 rows x 7 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_amt=pd.read_csv('./Purchase Redemption Data/task1_output2.csv')\n",
    "day_amt.rename(columns={'date':'mfd_date'},inplace=True)\n",
    "mfd=pd.merge(mfd,day_amt,on='mfd_date',how='left')\n",
    "mfd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "mfd['purchase_pct']=mfd['purchase_amt'].pct_change()\n",
    "mfd['redeem_pct']=mfd['redeem_amt'].pct_change()\n",
    "mfd['net_purchase']=mfd['purchase_amt']-mfd['redeem_amt']\n",
    "mfd['net_purchase_pct']=mfd['net_purchase'].pct_change()\n",
    "mfd['net_interest']=mfd['bao_pct']-mfd['bank_pct']\n",
    "mfd['net_interest_pct']=mfd['net_interest'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sign\n",
       " 1.0    165\n",
       "-1.0    127\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mfd.dropna(inplace=True)\n",
    "mfd1=mfd.copy()\n",
    "# plt.plot(mfd['net_purchase_pct'],label='net_purchase_pct')\n",
    "# plt.plot(mfd['bao_pct'],label='bao_pct')\n",
    "#求相关系数\n",
    "# mfd[['net_purchase_pct','bao_pct']].corr()\n",
    "# mfd[['net_purchase_pct','bank_pct']].corr()\n",
    "#统计net_interest_pct与net_purchase_pct的方向一致的次数\n",
    "\n",
    "#将net_purchase_pct下移一列\n",
    "mfd1['net_purchase_pct']=mfd1['net_purchase_pct'].shift(1)\n",
    "mfd1.dropna(inplace=True)\n",
    "mfd1\n",
    "mfd1['sign']=np.sign(mfd1['net_interest_pct']*mfd1['net_purchase_pct'])\n",
    "mfd1['sign'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mfd_date</th>\n",
       "      <th>Interest_1_W</th>\n",
       "      <th>mfd_7daily_yield</th>\n",
       "      <th>purchase_amt</th>\n",
       "      <th>redeem_amt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20130701</td>\n",
       "      <td>5.423</td>\n",
       "      <td>6.307</td>\n",
       "      <td>32488348</td>\n",
       "      <td>5525022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20130702</td>\n",
       "      <td>4.750</td>\n",
       "      <td>6.174</td>\n",
       "      <td>29037390</td>\n",
       "      <td>2554548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20130703</td>\n",
       "      <td>4.242</td>\n",
       "      <td>6.034</td>\n",
       "      <td>27270770</td>\n",
       "      <td>5953867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20130704</td>\n",
       "      <td>3.938</td>\n",
       "      <td>5.903</td>\n",
       "      <td>18321185</td>\n",
       "      <td>6410729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20130705</td>\n",
       "      <td>3.816</td>\n",
       "      <td>5.739</td>\n",
       "      <td>11648749</td>\n",
       "      <td>2763587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>20140825</td>\n",
       "      <td>3.363</td>\n",
       "      <td>4.139</td>\n",
       "      <td>309574223</td>\n",
       "      <td>312413411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>20140826</td>\n",
       "      <td>3.318</td>\n",
       "      <td>4.131</td>\n",
       "      <td>306945089</td>\n",
       "      <td>285478563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>20140827</td>\n",
       "      <td>3.349</td>\n",
       "      <td>4.123</td>\n",
       "      <td>302194801</td>\n",
       "      <td>468164147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>20140828</td>\n",
       "      <td>3.538</td>\n",
       "      <td>4.116</td>\n",
       "      <td>245082751</td>\n",
       "      <td>297893861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>20140829</td>\n",
       "      <td>3.672</td>\n",
       "      <td>4.123</td>\n",
       "      <td>267554713</td>\n",
       "      <td>273756380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mfd_date  Interest_1_W  mfd_7daily_yield  purchase_amt  redeem_amt\n",
       "0    20130701         5.423             6.307      32488348     5525022\n",
       "1    20130702         4.750             6.174      29037390     2554548\n",
       "2    20130703         4.242             6.034      27270770     5953867\n",
       "3    20130704         3.938             5.903      18321185     6410729\n",
       "4    20130705         3.816             5.739      11648749     2763587\n",
       "..        ...           ...               ...           ...         ...\n",
       "289  20140825         3.363             4.139     309574223   312413411\n",
       "290  20140826         3.318             4.131     306945089   285478563\n",
       "291  20140827         3.349             4.123     302194801   468164147\n",
       "292  20140828         3.538             4.116     245082751   297893861\n",
       "293  20140829         3.672             4.123     267554713   273756380\n",
       "\n",
       "[294 rows x 5 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.DataFrame(data=mfd,columns=['mfd_date','Interest_1_W','mfd_7daily_yield','purchase_amt','redeem_amt'])\n",
    "data.to_csv('task4_input.csv',index=False)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('task4_input.csv')\n",
    "data\n",
    "\n",
    "data['date']=pd.to_datetime(data['mfd_date'],format='%Y%m%d')\n",
    "data['week']=data['date'].dt.weekday\n",
    "data['week_num']=0\n",
    "\n",
    "#新增周数一列，表示第几周，从1开始，例如，2013-07-01到2013-07-05为第1周\n",
    "week_num=0\n",
    "for i in range(len(data)):\n",
    "    if data[\"week\"][i]==0:\n",
    "        week_num+=1\n",
    "        data['week_num'][i]=week_num\n",
    "    else:\n",
    "        data['week_num'][i]=week_num\n",
    "data.drop(columns=['mfd_date','date','week'],inplace=True)\n",
    "data.to_csv('task4_input2.csv',index=False,header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bank_rate</th>\n",
       "      <th>bao_rate</th>\n",
       "      <th>net_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bank_rate</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.344673</td>\n",
       "      <td>0.356458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bao_rate</th>\n",
       "      <td>0.344673</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.722588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>net_purchase</th>\n",
       "      <td>0.356458</td>\n",
       "      <td>0.722588</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              bank_rate  bao_rate  net_purchase\n",
       "bank_rate      1.000000  0.344673      0.356458\n",
       "bao_rate       0.344673  1.000000      0.722588\n",
       "net_purchase   0.356458  0.722588      1.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data=pd.read_csv('task4_output.csv')\n",
    "#计算相关系数\n",
    "data['bao_rate_pct']=data['bao_rate'].pct_change()\n",
    "data['purchase_pct']=data['purchase_avg'].pct_change()\n",
    "data['net_purchase']=data['purchase_avg']-data['redeem_avg']\n",
    "# data[['purchase_pct','bao_rate_pct']].corr()\n",
    "data[['bank_rate','bao_rate','purchase_avg','redeem_avg']].corr()\n",
    "data[['bank_rate','bao_rate','net_purchase']].corr()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
